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1.
Ngonmang Kaledje, Christel Blaise.
Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux.
Degree: Docteur es, Informatique, 2014, Paris 13
URL: http://www.theses.fr/2014PA132057
► Les réseaux sont présents dans plusieurs contextes et applications : biologie, transports, réseaux sociaux en ligne, etc. De nombreuses applications récentes traitent d'immenses volumes de…
(more)
▼ Les réseaux sont présents dans plusieurs contextes et applications : biologie, transports, réseaux sociaux en ligne, etc. De nombreuses applications récentes traitent d'immenses volumes de données personnelles. Les liens entre les personnes dans ces données peuvent traduire des liens d'amitiés, des échanges de messages, ou des intérêts communs. Les entités impliquées dans les réseaux, et spécialement les personnes, ont tendance à former des communautés. Dans ce contexte, une communauté peut être définie comme un ensemble d'entités qui interagissent beaucoup plus entre elles qu'avec le reste du réseau. La détection de communautés dans les grands réseaux a largement été étudiée pendant ces dernières années, suite aux travaux précurseurs de Newman qui a introduit le critère de modularité. Toutefois, la majorité des algorithmes de détection de communautés supposent que le réseau est complètement connu et qu'il n'évolue pas avec le temps. Dans cette thèse, nous commençons par proposer de nouvelles méthodes pour la détection de communautés locales (en considérant uniquement le voisinage d'un nœud donné et sans accéder à la totalité du réseau). Nos algorithmes sont plus efficaces que ceux de l'état de l'art. Nous montrons ensuite comment utiliser les communautés détectées pour améliorer la prévision de comportements utilisateurs. Dans un deuxième temps, nous proposons des approches pour prévoir l'évolution des communautés détectées. Ces méthodes sont basées sur des techniques d'apprentissage automatique. Enfin, nous proposons un framework général pour stocker et analyser les réseaux distribués dans un environnement "Big Data" . Les méthodes proposées sont validées en utilisant (entre autre) des données réelles issues d'un partenaire industriel fournissant un des réseaux en ligne les plus utilisés en France (40 millions d'utilisateurs).
Complex networks arises in many contexts and applications : biology, transports, online social networks (ONS). Many recent applications deal with large amount of personal data. The links between peoples may reflect freindship, messaging, or some common interests. Entities in complex network, and espacially persons, tend to form communities. Here, a community can be defined as a set of entities interacting more between each other than with the rest of the network. The topic of community detection in large networks as been extensively studied during the last decades, following the seminal work by newman, who popularized the modularity criteria. However, most community detection algorithms assume that the network is entirely known and that is does not evolve with time. This is usually not true in real world applications. In this thesis, we start by proposing novel methods for local community identification (considering only the vicinity of a given node, without accessing the whole graph). Our algorithms experimentally outperform the state-of-art methods. We show how to use the local communities to enhance the prediction of a user's behaviour. Secondly, we propose some approaches to predict…
Advisors/Committee Members: Viennet, Emmanuel (thesis director).
Subjects/Keywords: Détection de communautés; Community detection
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APA (6th Edition):
Ngonmang Kaledje, C. B. (2014). Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux. (Doctoral Dissertation). Paris 13. Retrieved from http://www.theses.fr/2014PA132057
Chicago Manual of Style (16th Edition):
Ngonmang Kaledje, Christel Blaise. “Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux.” 2014. Doctoral Dissertation, Paris 13. Accessed February 27, 2021.
http://www.theses.fr/2014PA132057.
MLA Handbook (7th Edition):
Ngonmang Kaledje, Christel Blaise. “Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux.” 2014. Web. 27 Feb 2021.
Vancouver:
Ngonmang Kaledje CB. Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux. [Internet] [Doctoral dissertation]. Paris 13; 2014. [cited 2021 Feb 27].
Available from: http://www.theses.fr/2014PA132057.
Council of Science Editors:
Ngonmang Kaledje CB. Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux. [Doctoral Dissertation]. Paris 13; 2014. Available from: http://www.theses.fr/2014PA132057

Cornell University
2.
Andrews, June.
Community Detection In Large Networks.
Degree: PhD, Applied Mathematics, 2012, Cornell University
URL: http://hdl.handle.net/1813/31046
► Graphs are used to represent various large and complex networks in scientific applications. In order to understand the structure of these graphs, it is useful…
(more)
▼ Graphs are used to represent various large and complex networks in scientific applications. In order to understand the structure of these graphs, it is useful to treat a set of nodes with similar characteristics as one
community and analyze the
community's behavior as a whole. Finding all such communities within the graph is the object of
community detection. In our research, we compare dozens of existing
community detection methods and develop a new class of algorithms for finding communities.
Advisors/Committee Members: Hopcroft, John E (chair), Kleinberg, Jon M (committee member), Strogatz, Steven H (committee member).
Subjects/Keywords: community detection; social networks
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APA ·
Chicago ·
MLA ·
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Export
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APA (6th Edition):
Andrews, J. (2012). Community Detection In Large Networks. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/31046
Chicago Manual of Style (16th Edition):
Andrews, June. “Community Detection In Large Networks.” 2012. Doctoral Dissertation, Cornell University. Accessed February 27, 2021.
http://hdl.handle.net/1813/31046.
MLA Handbook (7th Edition):
Andrews, June. “Community Detection In Large Networks.” 2012. Web. 27 Feb 2021.
Vancouver:
Andrews J. Community Detection In Large Networks. [Internet] [Doctoral dissertation]. Cornell University; 2012. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1813/31046.
Council of Science Editors:
Andrews J. Community Detection In Large Networks. [Doctoral Dissertation]. Cornell University; 2012. Available from: http://hdl.handle.net/1813/31046

University of Illinois – Urbana-Champaign
3.
Yuan, Yubai.
Approximate likelihood for dependent networks and hyperlink predictions.
Degree: PhD, Statistics, 2020, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/107868
► Network data has arisen as one of the most common forms of information collection. This is due to the fact that the scope of studies…
(more)
▼ Network data has arisen as one of the most common forms of information collection. This is due to the fact that the scope of studies not only focuses on subjects alone, but also on the relationships among subjects. In this thesis, we address two major challenges in the network analysis.
In the first part of the thesis, we focus on the
detection of
community structure in the network. In practical, within-
community members are more likely to be connected than between-
community members, which is also reflected in that the edges within a
community are intercorrelated. However, existing probabilistic models for
community detection such as the stochastic block model (SBM) are not designed to capture the dependence among edges. In the first part, we propose a novel
community detection approach to incorporate intra-
community dependence of connectivities through the Bahadur representation. The proposed method does not require specifying the likelihood function, which could be intractable for correlated binary connectivities. In addition, the proposed method allows for heterogeneity among edges among different communities. In theory, we show that incorporating correlation information can achieve a faster convergence rate compared to the independent SBM, and the proposed algorithm has a lower estimation bias and accelerated convergence speed compared to the variational EM. Our simulation studies show that the proposed algorithm outperforms the existing variational EM algorithm assuming conditional independence among edges. We also demonstrate the application of the proposed method to agricultural product trading networks from different countries.
In the second part, we focus on the joint prediction of pairwise link and hyperlink under multi-layer networks to incorporate high-order relations in network, which are not considered in the traditional graph representation models which only predict two-way pairwise relations. We propose a novel joint network embedding approach on simultaneously encoding pairwise links and hyper- links onto a latent space to capture the dependency between pairwise and multi-way links, which allows inference of potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to utilize high-order network information. In addition, the proposed method introduces the hierarchical dependency among links to infer potential hyperlinks, and leads to a better link prediction. In theory, we establish the estimation consistency for the proposed embedding approach, and provide a faster converge rate compared to hyperlink prediction using pairwise links only. Numerical studies on both simulation settings and Facebook ego-network show that the proposed method improves both hyperlink and pairwise link predictions accuracy compared to the existing link prediction methods.
Advisors/Committee Members: Qu, Annie (advisor), Qu, Annie (Committee Chair), Shao, Xiaofeng (committee member), Chen, Xiaohui (committee member), Yang, Yun (committee member).
Subjects/Keywords: Community detection; Hyperlink prediction
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Yuan, Y. (2020). Approximate likelihood for dependent networks and hyperlink predictions. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/107868
Chicago Manual of Style (16th Edition):
Yuan, Yubai. “Approximate likelihood for dependent networks and hyperlink predictions.” 2020. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 27, 2021.
http://hdl.handle.net/2142/107868.
MLA Handbook (7th Edition):
Yuan, Yubai. “Approximate likelihood for dependent networks and hyperlink predictions.” 2020. Web. 27 Feb 2021.
Vancouver:
Yuan Y. Approximate likelihood for dependent networks and hyperlink predictions. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2020. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2142/107868.
Council of Science Editors:
Yuan Y. Approximate likelihood for dependent networks and hyperlink predictions. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2020. Available from: http://hdl.handle.net/2142/107868

University of Illinois – Urbana-Champaign
4.
Xu, Jiaming.
Statistical inference in networks: fundamental limits and efficient algorithms.
Degree: PhD, 1200, 2015, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/72799
► Today witnesses an explosion of data coming from various types of networks such as online social networks and biological networks. The goal of this thesis…
(more)
▼ Today witnesses an explosion of data coming from various types of networks such as online social networks and
biological networks. The goal of this thesis is to understand when and how we can efficiently extract useful information from such network data.
In the first part, we are interested in finding tight-knit communities within a network.
Assuming the network is generated according to a planted cluster model, we derive a computationally efficient semidefinite programming relaxation of the maximum likelihood estimation method
and obtain a stronger performance guarantee than previously known.
If the
community sizes are linear in the total number of vertices, the guarantee matches up to a constant factor with the information limit which we also identify, and exactly matches without a constant gap when there is a single
community or two equal-sized communities. However, if the
community sizes are sublinear in the total number of vertices,
the guarantee is far from the information limit. We conjecture that our algorithm achieves the computational limit below which no
polynomial-time algorithm can succeed. To provide evidence, we show that finding a
community
in some regime below the conjectured computational limit but above the information limit is computationally intractable,
assuming hardness of the well-known planted clique problem.
The second part studies the problem of inferring the group preference for a set of items
based on the partial rankings over different subsets of the items provided by a group of users. A question of particular interest is how to optimally construct the graph used for assigning items to users for ranking. Assuming the partial rankings are generated independently according to the Plackett-Luce model, we analyze
computationally efficient estimators based on maximum likelihood and rank-breaking schemes that decompose partial rankings into pairwise comparisons. We provide upper and lower bounds on the estimation error. The lower bound depends on the degree sequence of the assignment graph, while the upper bound depends on the spectral gap of the assignment graph. When the graph is an expander, the lower and upper bounds match up to a logarithmic factor.
The unifying theme for the two parts of the thesis is the spectral gap of the graph. In both cases, when the graph has a large spectral gap, accurate and efficient inference is possible via maximum likelihood estimation or its convex relaxation. However, when the spectral gap vanishes, accurate inference may be statistically
impossible, or it is statistically possible but may be computationally intractable.
Advisors/Committee Members: Hajek, Bruce (advisor), Hajek, Bruce (Committee Chair), Srikant, R. (committee member), Oh, Sewoong (committee member), Sanghavi, Sujay (committee member), Lelarge, Marc (committee member).
Subjects/Keywords: Community detection; Networks; Statistical inference
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Xu, J. (2015). Statistical inference in networks: fundamental limits and efficient algorithms. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72799
Chicago Manual of Style (16th Edition):
Xu, Jiaming. “Statistical inference in networks: fundamental limits and efficient algorithms.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 27, 2021.
http://hdl.handle.net/2142/72799.
MLA Handbook (7th Edition):
Xu, Jiaming. “Statistical inference in networks: fundamental limits and efficient algorithms.” 2015. Web. 27 Feb 2021.
Vancouver:
Xu J. Statistical inference in networks: fundamental limits and efficient algorithms. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2142/72799.
Council of Science Editors:
Xu J. Statistical inference in networks: fundamental limits and efficient algorithms. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72799

University of Texas – Austin
5.
Zhang, Lingjia.
Community detection in network analysis: a survey.
Degree: MSin Statistics, Statistics, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/41634
► The existence of community structures in networks is not unusual, including in the domains of sociology, biology, and business, etc. The characteristic of the community…
(more)
▼ The existence of
community structures in networks is not unusual, including in the domains of sociology, biology, and business, etc. The characteristic of the
community structure is that nodes of the same
community are highly similar while on the contrary, nodes across communities present low similarity.
In academia, there is a surge in research efforts on
community detection in network analysis, especially in developing statistically sound methodologies for exploring, modeling, and interpreting these kind of structures and relationships.
This survey paper aims to provide a brief review of current applicable
statistical methodologies and approaches in a comparative manner along with metrics for evaluating graph clustering results and application using R. At the
end, we provide promising future research directions.
Advisors/Committee Members: Lin, Lizhen, Ph.D. (advisor), Keitt, Timothy (committee member).
Subjects/Keywords: Network analysis; Community detection; Clustering
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhang, L. (2016). Community detection in network analysis: a survey. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/41634
Chicago Manual of Style (16th Edition):
Zhang, Lingjia. “Community detection in network analysis: a survey.” 2016. Masters Thesis, University of Texas – Austin. Accessed February 27, 2021.
http://hdl.handle.net/2152/41634.
MLA Handbook (7th Edition):
Zhang, Lingjia. “Community detection in network analysis: a survey.” 2016. Web. 27 Feb 2021.
Vancouver:
Zhang L. Community detection in network analysis: a survey. [Internet] [Masters thesis]. University of Texas – Austin; 2016. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2152/41634.
Council of Science Editors:
Zhang L. Community detection in network analysis: a survey. [Masters Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/41634

Cornell University
6.
Kloumann, Isabel.
Behaviors, Interactions, And Communities In Networks.
Degree: PhD, Applied Mathematics, 2016, Cornell University
URL: http://hdl.handle.net/1813/44315
► Exciting and unexpected patterns can emerge when systems are highly connected, even when they are composed of the simplest objects. In this thesis we investigate…
(more)
▼ Exciting and unexpected patterns can emerge when systems are highly connected, even when they are composed of the simplest objects. In this thesis we investigate how networks of people, oscillators, apps, and nodes can be better understood through the behavior of the communities that emerge from their close interactions. Work in this thesis first examines how recent advances in dynamical systems have shed new light on the macrobehavior of networks of coupled oscillators. The dimensionality of the system is greatly reduced by viewing the system not as one of individual coupled oscillators, but as one of a smaller number of interacting groups. We demonstrate that the corresponding governing equations can be solved exactly. This thesis then investigates the seed set expansion problem, or how to uncover the local
community structure hidden around nodes, in social and other real-world networks. We explore how topological properties of communities and seed sets correlate with algorithm performance, and explain these empirical observations with theoretical ones. We then turn our focus back to a theoretical setting and develop a principled framework for evaluating ranking methods by studying seed set expansion applied to the stochastic block model. We derive the optimal gradient for separating the two classes of nodes in a stochastic block model, and find, surprisingly, that it is asymptotically equivalent to personalized PageRank. This connection provides a novel formal motivation for the success of personalized PageRank in seed set expansion and node ranking generally. We then leverage this framework to develop several theoretically motivated heuristics that incorporate higher moments of landing probabilities, and show that these techniques yield much stronger performance on seed set expansion for stochastic block models. Work in the second part of this thesis discusses two other highly connected networks, the Facebook social network, and the network of communication between researchers in a series of massive collaborations. In the first case we develop a retention model that accurately models users' tendencies to continue using apps, and at the social level we organize apps along two fundamental axes - popularity and sociality - and show how a user's probability of adopting an app depends on properties of both the local network structure and the match between the user's attributes, their friends' attributes, and the dominant attributes within the app user population. We show how our models give rise to compact sets of features with strong performance in predicting app success. In the second case we study a series of massive online collaborations of professional and amateur mathematicians, who collectively attempt to solve open problems in mathematics research. We identify interesting patterns in the linguistic structure and social reactions that distinguish important research contributions from less important ones. We also observe distinct changes in the language behavior, and the structure and timing of interactions…
Advisors/Committee Members: Kleinberg,Jon M. (chair), Mueller,Erich (committee member), Strogatz,Steven H (committee member).
Subjects/Keywords: Community detection; Machine learning; Data mining
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kloumann, I. (2016). Behaviors, Interactions, And Communities In Networks. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/44315
Chicago Manual of Style (16th Edition):
Kloumann, Isabel. “Behaviors, Interactions, And Communities In Networks.” 2016. Doctoral Dissertation, Cornell University. Accessed February 27, 2021.
http://hdl.handle.net/1813/44315.
MLA Handbook (7th Edition):
Kloumann, Isabel. “Behaviors, Interactions, And Communities In Networks.” 2016. Web. 27 Feb 2021.
Vancouver:
Kloumann I. Behaviors, Interactions, And Communities In Networks. [Internet] [Doctoral dissertation]. Cornell University; 2016. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1813/44315.
Council of Science Editors:
Kloumann I. Behaviors, Interactions, And Communities In Networks. [Doctoral Dissertation]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44315

Penn State University
7.
Adi, Mohammad.
Using Ants to Find Communities in Complex Networks.
Degree: 2014, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/21544
► Many systems arising in different fields can be described as complex networks, a collection of nodes and edges connecting nodes. An interesting property of these…
(more)
▼ Many systems arising in different fields can be described as complex networks, a collection of nodes and edges connecting nodes. An interesting property of these complex networks is the presence of communities (or clusters), which represent subsets of nodes within the network such that the number of edges between nodes in the same
community is large whereas the number of edges connecting nodes in different communities is small. In this thesis, we give an ant-based algorithm for finding communities in complex networks. We employ artificial ants to traverse the network based on a set of rules in order to discover a ``good set'' of edges that are likely to connect nodes within a
community. Using these edges we construct the communities after which local optimization methods are used to further improve the solution quality. Experimental results on a total of 136 problem instances that include various synthetic and real world complex networks show that the algorithm is very competitive against current state-of-the-art techniques for
community detection. In particular, our algorithm is more robust than existing algorithms as it performs well across many different types of networks.
Advisors/Committee Members: Thang Nguyen Bui, Thesis Advisor/Co-Advisor.
Subjects/Keywords: ant-algorithms; complex-networks; community-detection
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Adi, M. (2014). Using Ants to Find Communities in Complex Networks. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/21544
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Adi, Mohammad. “Using Ants to Find Communities in Complex Networks.” 2014. Thesis, Penn State University. Accessed February 27, 2021.
https://submit-etda.libraries.psu.edu/catalog/21544.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Adi, Mohammad. “Using Ants to Find Communities in Complex Networks.” 2014. Web. 27 Feb 2021.
Vancouver:
Adi M. Using Ants to Find Communities in Complex Networks. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Feb 27].
Available from: https://submit-etda.libraries.psu.edu/catalog/21544.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Adi M. Using Ants to Find Communities in Complex Networks. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/21544
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Toronto
8.
Benoit, David Martin.
Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling.
Degree: 2017, University of Toronto
URL: http://hdl.handle.net/1807/77757
► Regardless of sampling effort, it is rare to detect all individuals or species in a given survey. This issue, more commonly known as imperfect detection,…
(more)
▼ Regardless of sampling effort, it is rare to detect all individuals or species in a given survey. This issue, more commonly known as imperfect detection, can have negative impacts on data quality and interpretation, most notably leading to false absences for rare or difficult-to-detect species. In this study, I set out to determine the impacts of imperfect detection on estimates of species richness and community structure in a stream fish assemblage. Multi-species occupancy modelling was used to estimate species-specific occurrence probabilities while accounting for imperfect detection, thus creating a more informed dataset. This dataset was then compared to the original to see where differences occurred. In my analyses, I demonstrated that imperfect detection can lead to large changes in estimates of species richness at the site level and summarized differences in the community structure and sampling locations, represented through correspondence analyses.
M.Sc.
Advisors/Committee Members: Jackson, Donald, Ridgway, Mark, Ecology and Evolutionary Biology.
Subjects/Keywords: Community; Detection; Modelling; Occupancy; Stream; 0329
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Benoit, D. M. (2017). Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/77757
Chicago Manual of Style (16th Edition):
Benoit, David Martin. “Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling.” 2017. Masters Thesis, University of Toronto. Accessed February 27, 2021.
http://hdl.handle.net/1807/77757.
MLA Handbook (7th Edition):
Benoit, David Martin. “Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling.” 2017. Web. 27 Feb 2021.
Vancouver:
Benoit DM. Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling. [Internet] [Masters thesis]. University of Toronto; 2017. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1807/77757.
Council of Science Editors:
Benoit DM. Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling. [Masters Thesis]. University of Toronto; 2017. Available from: http://hdl.handle.net/1807/77757

University of Cambridge
9.
Olan, Ioana.
Regulatory Landscapes Associated With Changes In Nuclear Architecture.
Degree: PhD, 2019, University of Cambridge
URL: https://www.repository.cam.ac.uk/handle/1810/293014https://www.repository.cam.ac.uk/bitstream/1810/293014/4/license.txt
;
https://www.repository.cam.ac.uk/bitstream/1810/293014/5/7d3f0a25-0019-458d-b173-d4b0d6384685.zip
;
https://www.repository.cam.ac.uk/bitstream/1810/293014/6/PhD_onlinesubmission_IO.pdf.txt
;
https://www.repository.cam.ac.uk/bitstream/1810/293014/7/PhD_onlinesubmission_IO.pdf.jpg
► Gene regulation is essential for establishing a cell’s identity and relation with surrounding cells. A major role in modulating gene regulation is played by the…
(more)
▼ Gene regulation is essential for establishing a cell’s identity and relation with surrounding cells. A major role in modulating gene regulation is played by the cell’s chromatin architecture. Gene positioning relative to the center of the nucleus or its three-dimensional contacts with other genomic regions can determine whether the gene is expressed or not. Changes in chromatin architecture have been reported in cell differentiation contexts, where the gene expression landscape undergoes striking remodelling. Architectural changes leading to abnormal expression levels have also been reported and linked to diseases, such as dementia or cancer. Chromatin is organised hierarchically, with interactions between regulatory regions such as enhancers and promoters being hosted within megabase-scale domains. Enhancer-promoter alterations have been mostly described in differentiation scenarios, while a number of studies concluded that such interactions are pre-looped within the same lineage. In this study, I characterised chromatin architecture coupled with gene expression changes in RAS-induced senescent (RIS) cells. RIS is a tumour suppressive phenotype associated with cell cycle arrest and inflammation, associated with substantial alterations in both chromatin architecture and expression profile. By combining data from Hi-C, ChIP-Seq and RNA-Seq experiments, I showed that enhancer-promoter interactions were altered during RIS. In particular, pro-inflammatory genes in the IL1 locus showed increased interactions with enhancers. I showed that chromatin alterations also occurred at larger scale, re-arranging the chromatin hierarchy. I used graph theoretical approaches to model the hierarchical organisation of the genome. I linked large scale re-arrangements in RIS to the formation of Senescence Associated Heterochromatic Foci (SAHF), an important phenotypic feature of RIS, consisting of striking re-organisation of heterochromatic regions. In terms of network connectivity, RIS interactions were characterised by increased separation between any two genomic regions. The present study extends the current knowledge regarding the potential for alterations of enhancer-promoter interactions within the same lineage. It also introduces new tools for characterising the chromatin hierarchy and determining multiple layers of organisation and their association. Such tools have the potential of resolving chromosome relative positioning and contextualising the consequences of copy number alterations.
Subjects/Keywords: senescence; chromatin; network; gene regulation; community detection
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Olan, I. (2019). Regulatory Landscapes Associated With Changes In Nuclear Architecture. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/293014https://www.repository.cam.ac.uk/bitstream/1810/293014/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/293014/5/7d3f0a25-0019-458d-b173-d4b0d6384685.zip ; https://www.repository.cam.ac.uk/bitstream/1810/293014/6/PhD_onlinesubmission_IO.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/293014/7/PhD_onlinesubmission_IO.pdf.jpg
Chicago Manual of Style (16th Edition):
Olan, Ioana. “Regulatory Landscapes Associated With Changes In Nuclear Architecture.” 2019. Doctoral Dissertation, University of Cambridge. Accessed February 27, 2021.
https://www.repository.cam.ac.uk/handle/1810/293014https://www.repository.cam.ac.uk/bitstream/1810/293014/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/293014/5/7d3f0a25-0019-458d-b173-d4b0d6384685.zip ; https://www.repository.cam.ac.uk/bitstream/1810/293014/6/PhD_onlinesubmission_IO.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/293014/7/PhD_onlinesubmission_IO.pdf.jpg.
MLA Handbook (7th Edition):
Olan, Ioana. “Regulatory Landscapes Associated With Changes In Nuclear Architecture.” 2019. Web. 27 Feb 2021.
Vancouver:
Olan I. Regulatory Landscapes Associated With Changes In Nuclear Architecture. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2021 Feb 27].
Available from: https://www.repository.cam.ac.uk/handle/1810/293014https://www.repository.cam.ac.uk/bitstream/1810/293014/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/293014/5/7d3f0a25-0019-458d-b173-d4b0d6384685.zip ; https://www.repository.cam.ac.uk/bitstream/1810/293014/6/PhD_onlinesubmission_IO.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/293014/7/PhD_onlinesubmission_IO.pdf.jpg.
Council of Science Editors:
Olan I. Regulatory Landscapes Associated With Changes In Nuclear Architecture. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/293014https://www.repository.cam.ac.uk/bitstream/1810/293014/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/293014/5/7d3f0a25-0019-458d-b173-d4b0d6384685.zip ; https://www.repository.cam.ac.uk/bitstream/1810/293014/6/PhD_onlinesubmission_IO.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/293014/7/PhD_onlinesubmission_IO.pdf.jpg

Indiana University
10.
Gao, Zheng.
COMMUNITY DETECTION IN GRAPHS
.
Degree: 2020, Indiana University
URL: http://hdl.handle.net/2022/25623
► Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims…
(more)
▼ Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach,
community detection aims to explore node high-order closeness by leveraging graph topological structure. By grouping similar nodes or edges into the same
community while separating dissimilar ones apart into different communities, graph structure can be revealed in a coarser resolution. It can be beneficial for numerous applications such as user shopping recommendation and advertisement in e-commerce, protein-protein interaction prediction in the bioinformatics, and literature recommendation or scholar collaboration in citation
analysis. However, identifying communities is an ill-defined problem. Due to the No Free Lunch theorem [1], there is neither gold standard to represent perfect
community partition nor universal methods that are able to detect satisfied communities for all tasks under various types of graphs. To have a global view of this research topic, I summarize state-of-art
community detection methods by categorizing them based on graph types, research tasks and methodology frameworks. As academic exploration on
community detection grows rapidly in recent years, I hereby particularly focus on the state-of-art works published in the latest decade, which may leave out some classic models published decades ago. Meanwhile, three subtle
community detection tasks are proposed and assessed in this dissertation as well. First, apart from general models which consider only graph structures, personalized
community detection considers user need as auxiliary information to guide
community detection. In the end, there will be fine-grained communities for nodes better matching user needs while coarser-resolution communities for the rest of less relevant nodes. Second, graphs always suffer from the sparse connectivity issue. Leveraging conventional models directly on such graphs may hugely distort the quality of generate communities. To tackle such a problem, cross-graph techniques are involved to propagate external graph information as a support for target graph
community detection. Third, graph
community structure supports a natural language processing (NLP) task to depict node intrinsic characteristics by generating node summarizations via a text generative model. The contribution of this dissertation is threefold. First, a decent amount of researches are reviewed and summarized under a well-defined taxonomy. Existing works about methods, evaluation and applications are all addressed in the literature review. Second, three novel
community detection tasks are demonstrated and associated models are proposed and evaluated by comparing with state-of-art baselines under various datasets. Third, the limitations of current works are pointed out and future research tracks with potentials are discussed as well.
Advisors/Committee Members: Liu, Xiaozhong (advisor).
Subjects/Keywords: community detection;
complex network analysis;
graph mining
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gao, Z. (2020). COMMUNITY DETECTION IN GRAPHS
. (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/25623
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Gao, Zheng. “COMMUNITY DETECTION IN GRAPHS
.” 2020. Thesis, Indiana University. Accessed February 27, 2021.
http://hdl.handle.net/2022/25623.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Gao, Zheng. “COMMUNITY DETECTION IN GRAPHS
.” 2020. Web. 27 Feb 2021.
Vancouver:
Gao Z. COMMUNITY DETECTION IN GRAPHS
. [Internet] [Thesis]. Indiana University; 2020. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2022/25623.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Gao Z. COMMUNITY DETECTION IN GRAPHS
. [Thesis]. Indiana University; 2020. Available from: http://hdl.handle.net/2022/25623
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Adelaide
11.
Le, Ba Dung.
Community detection in complex networks.
Degree: 2018, University of Adelaide
URL: http://hdl.handle.net/2440/117956
► Complex networks such as social networks and biological networks represent complex systems in the real world. These networks usually consist of communities which are groups…
(more)
▼ Complex networks such as social networks and biological networks represent complex systems in the real world. These networks usually consist of communities which are groups of nodes with dense connections among nodes in the same group and sparse connections between nodes in different groups. Identifying communities in complex networks is useful for many real-world applications. Numerous
community detection approaches have been investigated over the past decades. Modularity is a well-known function to measure the quality of a network division into communities. The most popular
detection approach is modularity optimization that identifes communities by finding the
community division with highest modularity over all possible
community divisions of the network. Current state-of-the-art algorithms for maximizing modularity perform well on networks of strong communities, which have more intra-
community connections than inter-
community connections. However, these algorithms tend to get trapped in a poor local maximum on networks with weak communities, which have more inter-
community connections than intra-
community connections. In the first part of this thesis, we develop a new algorithm for maximizing modularity in networks with weak communities. Our proposed algorithm extends the state-of-the-art algorithm LPAm+ by introducing a method to escape local maximum. Our algorithm follows a guided search strategy inspired by the record-to- record travel algorithm for a trade-off between performance and complexity. Experimental results show that our proposed algorithm, named meta-LPAm+, outperforms state-of-the-art algorithms, in terms of modularity, on networks with weak communities while retaining a comparable performance on networks of strong communities. In the second part of this thesis, we study the problem of evaluating
community detection algorithms. Evaluating the
detection algorithms on networks with known communities is important to estimate the accuracy of the algorithms and to compare different algorithms. Since there are currently only a small number of real networks with known communities available, the
detection algorithms are most dominantly tested on synthetic networks with built-in
community structure. Current benchmarks, that generate networks with built-in
community structure, assign the same fraction of inter-
community connections, referred to as the mixing fraction, for every
community in the same network and ignore the presence of noise, or outliers. These existing benchmarks, therefore, cannot capture properties of nodes and communities in real networks. We address this issue by proposing a new benchmark that accounts for the heterogeneity in
community mixing fractions and the presence of outliers. Our proposed benchmark extends the state-of-the-art benchmark LFR by incorporating heterogeneous
community mixing fractions and outliers. We use our new benchmark to evaluate the performances of existing
community detection algorithms. The results show that the variation in
community mixing fractions and…
Advisors/Committee Members: Shen, Hong (advisor), Falkner, Nickolas (advisor), Nguyen, Hung (advisor), School of Computer Science (school).
Subjects/Keywords: Community detection; complex networks; network clustering
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Le, B. D. (2018). Community detection in complex networks. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/117956
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Le, Ba Dung. “Community detection in complex networks.” 2018. Thesis, University of Adelaide. Accessed February 27, 2021.
http://hdl.handle.net/2440/117956.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Le, Ba Dung. “Community detection in complex networks.” 2018. Web. 27 Feb 2021.
Vancouver:
Le BD. Community detection in complex networks. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2440/117956.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Le BD. Community detection in complex networks. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/117956
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Colorado State University
12.
Buhnerkempe, Michael G.
Using community detection on networks to identify migratory bird flyways in North America.
Degree: MS(M.S.), Statistics, 2012, Colorado State University
URL: http://hdl.handle.net/10217/71658
► Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the…
(more)
▼ Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 years. However, recent desires to incorporate uncertainty regarding biological processes into an adaptive harvest management program have underscored the need to re-evaluate the traditional flyway concept and bring uncertainty in flyways themselves into management planning. Here, we use bird band and recovery data to develop a network model of migratory movement for four waterfowl species, mallard (Anas platyrhnchos), northern pintail (A. acuta), American green-winged teal (A. carolinensis), and Canada Goose (Branta Canadensis) in North America. A
community detection algorithm is then used to identify migratory flyways. Additionally, we compare flyway structure both across species and through time to determine broad applicability of the previous flyway concept. We also propose a novel metric, the consolidation factor, to describe a node's (i.e., small geographic area) importance in determining flyway structure. The
community detection algorithm identified four main flyways for mallards, northern pintails, and American green-winged teal with the flyway structure of Canada geese exhibiting higher complexity. For mallards, flyway structure was relatively consistent through time. However, consolidation factors and cross-
community mixing patterns revealed that for mallards and green-winged teal the presumptive Mississippi flyway was potentially a zone of high mixing between flyways. Additionally, interspersed throughout these major flyways were smaller mixing zones that point to added complexity and uncertainty in the four-flyway concept. Not only does the incorporation of this uncertainty due to mixing provide a potential alternative management strategy, but the network approach provides a robust, quantitative approach to flyway identification that fits well with the adaptive harvest management framework currently used in North American waterfowl management.
Advisors/Committee Members: Hoeting, Jennifer A. (advisor), Givens, Geof H. (committee member), Webb, Colleen T. (committee member).
Subjects/Keywords: consolidation factor; waterfowl; network; flyways; community detection
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Buhnerkempe, M. G. (2012). Using community detection on networks to identify migratory bird flyways in North America. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/71658
Chicago Manual of Style (16th Edition):
Buhnerkempe, Michael G. “Using community detection on networks to identify migratory bird flyways in North America.” 2012. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/71658.
MLA Handbook (7th Edition):
Buhnerkempe, Michael G. “Using community detection on networks to identify migratory bird flyways in North America.” 2012. Web. 27 Feb 2021.
Vancouver:
Buhnerkempe MG. Using community detection on networks to identify migratory bird flyways in North America. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/71658.
Council of Science Editors:
Buhnerkempe MG. Using community detection on networks to identify migratory bird flyways in North America. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/71658

Delft University of Technology
13.
Huang, H. (author).
Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm.
Degree: MSComputer Science, 2015, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d
► Complex networks are a special type of graph that frequently appears in nature and in many different fields of science and engineering. Studying complex networks…
(more)
▼ Complex networks are a special type of graph that frequently appears in nature and in many different fields of science and engineering. Studying complex networks is the key to solve the problems in these fields. Complex networks have unique features which we cannot find in regular graphs, and the study of complex networks gives rise to many interesting research questions. An interesting feature to study in complex networks is community structure. Intuitively speaking, communities are group of vertices in a graph that are densely connected with each other in the same group, while sparsely connected with other nodes in the graph. The notion of community has practical significance. Many different concept and phenomenons in real world problems can be translated into communities in a graph, such as politicians with similar opinions in the political opinion network. In this thesis work, a distributed version of a popular community detection method-Louvain method-is developed using graph computation framework Apache Spark GraphX. Characteristics of this algorithm, such as convergence and quality of communities produced, are studied by both theoretical reasoning and experimental evaluation. The result shows that this algorithm can parallelize community detection effectively. This thesis also explores the possibility of using graph sampling to accelerate resolution parameter selection of a resolution-limit-free community detection method. Two sampling algorithms, random node selection and forest fire sampling algorithm, are compared. This comparison leads to suggestions of choice of sampling algorithm and parameter value of the chosen sampling algorithm.
Master of Science Computer Science
Software and Computer Technology
Electrical Engineering, Mathematics and Computer Science
Advisors/Committee Members: Hidders, A.J.H. (mentor), Krings, G. (mentor).
Subjects/Keywords: complex network; community detection; distributed computing
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Huang, H. (. (2015). Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d
Chicago Manual of Style (16th Edition):
Huang, H (author). “Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm.” 2015. Masters Thesis, Delft University of Technology. Accessed February 27, 2021.
http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d.
MLA Handbook (7th Edition):
Huang, H (author). “Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm.” 2015. Web. 27 Feb 2021.
Vancouver:
Huang H(. Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Feb 27].
Available from: http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d.
Council of Science Editors:
Huang H(. Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d

University of Sydney
14.
Amiri, Babak.
Evolutionary Algorithms for Community Detection in Complex Networks
.
Degree: 2013, University of Sydney
URL: http://hdl.handle.net/2123/10451
► In recent years there has been a surge of community detection study of complex network analysis, since communities often play important roles in network systems.…
(more)
▼ In recent years there has been a surge of community detection study of complex network analysis, since communities often play important roles in network systems. Most contemporary community detection algorithms employ single optimization criteria (i.e., modularity), which may not be adequate to represent the structures in complex networks. We suggest a community detection process as a Multi-Objective Optimization Problem (MOP) for investigating the community structures in complex networks. To overcome the limitations of community detection problems, we propose new multi-objective optimization algorithms: a Modified Harmony Search Algorithm, a Hybrid Chaotic Local Search-Harmony Search Algorithm (CLS-HAS) and an Enhanced Firefly Algorithm (EFF). A new tuning parameter based on a chaotic mechanism and novel self-adaptive probabilistic mutation strategies is used to improve the overall performance of the EFF algorithm. Although much of the focus of community detection techniques has been on identifying disjoint and static communities, almost all real networks are dynamic in nature. Detecting communities in dynamic networks is very challenging and the analysis of dynamic communities is still considered to be in its infancy. To study the structure of communities in dynamic networks, we consider an evolution-based clustering method with the aim of maximizing cluster accuracy and minimizing clustering drift from one time step to the next. In this study, the detection of communities with temporal smoothness is formulated as a multi-objective problem and the Modified Bee Swarm Optimization (MBSO) is proposed to solve the community detection problem. The MBSO algorithm uses three kinds of bees, which have different moving pattern, to explore the entire search space and prevent premature convergence. The proposed algorithm has several remarkable characteristics to enhance the search capability of the original bee swarm optimization (BSO) for finding Pareto optimal solutions. Many real networks have complex overlapping community structures. This research also proposes a novel Fungi Optimization Algorithm (FOA) to discover overlapping communities. Unlike conventional algorithms based on node clustering, the proposed algorithm is based on link clustering.
Subjects/Keywords: Community Detection;
Evolutionary Algorithms;
Modularity;
Social Networks
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Amiri, B. (2013). Evolutionary Algorithms for Community Detection in Complex Networks
. (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/10451
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Amiri, Babak. “Evolutionary Algorithms for Community Detection in Complex Networks
.” 2013. Thesis, University of Sydney. Accessed February 27, 2021.
http://hdl.handle.net/2123/10451.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Amiri, Babak. “Evolutionary Algorithms for Community Detection in Complex Networks
.” 2013. Web. 27 Feb 2021.
Vancouver:
Amiri B. Evolutionary Algorithms for Community Detection in Complex Networks
. [Internet] [Thesis]. University of Sydney; 2013. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2123/10451.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Amiri B. Evolutionary Algorithms for Community Detection in Complex Networks
. [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/10451
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech
15.
Senthil, Rathna.
IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.
Degree: MS, Computer Science and Applications, 2016, Virginia Tech
URL: http://hdl.handle.net/10919/65160
► Complex systems in areas such as biology, physics, social science, and technology are extensively modeled as networks due to the rich set of tools available…
(more)
▼ Complex systems in areas such as biology, physics, social science, and technology are extensively
modeled as networks due to the rich set of tools available for their study and analysis. In such
networks, groups of nodes that correspond to functional units or those that share some common
attributes result in densely connected structures called communities.
Community formation is an
inherent process, and it is not easy to detect these structures because of the complex ways in which components of these systems interact.
Detecting communities in complex networks is important because it helps us to understand
their internal dynamics better, thereby leading to significant insights into the underlying systems.
Overlapping communities are formed when nodes in the network simultaneously belong to more
than one
community, and it has been shown that most real networks naturally contain such an overlapping
community structure. In this thesis, I introduce a new approach to overlapping
community detection called IDLE that incorporates ideas from another interesting problem: the identification of influential spreaders. Influential spreaders are nodes that play an important role in the propagation of information or diseases in networks. Research suggests that the main core identified by k-core decomposition techniques are the most influential spreaders. In my approach, I use these k-cores as candidate seeds for local
community detection. Following a well-defined seed selection process, IDLE builds and prunes their corresponding local communities. It then augments the resulting local communities and puts them together to obtain the global overlapping
community
structure of the network.
My approach improves on the current local
community detection techniques, because they use
either random nodes or maximal k-cliques as seeds, and they do not focus explicitly on detecting
overlapping nodes in the network. Hence their results can be significantly improved in building
ground-truth overlapping communities. The results of my experiments on real and synthetic networks indicate that IDLE results in enhanced overlapping
community detection and thereby a
better identification of overlapping nodes that could be important or influential components in the
underlying system.
Advisors/Committee Members: Heath, Lenwood S. (committeechair), Prakash, B. Aditya (committee member), Raghvendra, Sharath (committee member).
Subjects/Keywords: Overlapping Community Detection; Complex Networks; Local Expansion
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Senthil, R. (2016). IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/65160
Chicago Manual of Style (16th Edition):
Senthil, Rathna. “IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.” 2016. Masters Thesis, Virginia Tech. Accessed February 27, 2021.
http://hdl.handle.net/10919/65160.
MLA Handbook (7th Edition):
Senthil, Rathna. “IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.” 2016. Web. 27 Feb 2021.
Vancouver:
Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10919/65160.
Council of Science Editors:
Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/65160

Australian National University
16.
Sun, Xufei.
Efficient Community Detection
.
Degree: 2015, Australian National University
URL: http://hdl.handle.net/1885/16471
► Given a large network, local community detection aims at finding the community that contains a set of query nodes and also maximises (minimises) a goodness…
(more)
▼ Given a large network, local community detection aims at finding the community
that contains a set of query nodes and also maximises (minimises) a goodness metric.
Furthermore, due to the inconvenience or impossibility of obtaining the complete
network information in many situations, the detection becomes more challenging.
This problem has recently drawn intense research interest. Various goodness metrics
have been proposed. And most of them base on the statistical features of community
structures, such as the internal density or external spareness. However, the
metrics often result in unsatisfactory results by either including irrelevant subgraphs
of high density, or pulling in outliers which accidentally match the metric for the time
being. Further more, when in a highly overlapping environment such as social networks,
the unconventional community structures make these metrics usually end up
with a quite trivial detection result.
In our work, we go for a alternative point of view on the formation of the communities,
namely the assembly of nodes with different roles in the structure. With
the new view point, we present two metrics which are proved to perform superiorly
in traditional and complex environment respectively. Moreover, on realising a single
metric is whatsoever limited in effectiveness as well as scope of application, we raise
up a complete framework for the collaboration ofmetrics in the field, which also lands
a base-stone for future innovations.
The experiment results collected from Amazon, DBLP, Youtube and LivingJournal
well certifies the effectiveness of the metrics.
Subjects/Keywords: community detection;
large graph;
community;
local detection;
metric;
goodness metric;
overlapping graphs
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sun, X. (2015). Efficient Community Detection
. (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/16471
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sun, Xufei. “Efficient Community Detection
.” 2015. Thesis, Australian National University. Accessed February 27, 2021.
http://hdl.handle.net/1885/16471.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sun, Xufei. “Efficient Community Detection
.” 2015. Web. 27 Feb 2021.
Vancouver:
Sun X. Efficient Community Detection
. [Internet] [Thesis]. Australian National University; 2015. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1885/16471.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sun X. Efficient Community Detection
. [Thesis]. Australian National University; 2015. Available from: http://hdl.handle.net/1885/16471
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Utah
17.
Yadav, Nitin.
Community-affinity: measuring strength of memberships of nodes in network communities.
Degree: MSin Computing, School of Computing, 2015, University of Utah
URL: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497
► Detecting community structure in networks has been a widely studied area. While mostof the methods produce an exclusive membership of the nodes, the nodes in…
(more)
▼ Detecting community structure in networks has been a widely studied area. While mostof the methods produce an exclusive membership of the nodes, the nodes in real-worldnetworks tend to partially belong to more than one community. In this thesis, we studysome methods that have been used to quantify the strength of memberships of nodes indi erent communities (or community-a nity, as we call it) and also de ne three of our ownmethods. Our rst method is based on personalized PageRanks of the nodes, the secondis based on the individual contribution of nodes to the modularity of the graph, and thelast is based on the common neighborhood between two nodes. We rst discuss di erentnotions of community-a nity, each of which is followed by formulations that capture thatnotion. We then discuss the concept of stability, which uses community-a nity scores of thenodes to compute how "stable" each node is in a given community structure and how wecan use this information in estimating the quality of a given community structure. Towardsthe end, we introduce a community detection algorithm, which "peels" communities one byone from a graph. The results of our experiments show that our algorithm is very accurateeven for a large number of nodes in a graph. Our algorithm is fast and it performs verywell on real-world graphs compared to the state of the art algorithms.
Subjects/Keywords: community-affinity; community-detection; graph clustering; graphs; network science
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Yadav, N. (2015). Community-affinity: measuring strength of memberships of nodes in network communities. (Masters Thesis). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497
Chicago Manual of Style (16th Edition):
Yadav, Nitin. “Community-affinity: measuring strength of memberships of nodes in network communities.” 2015. Masters Thesis, University of Utah. Accessed February 27, 2021.
http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497.
MLA Handbook (7th Edition):
Yadav, Nitin. “Community-affinity: measuring strength of memberships of nodes in network communities.” 2015. Web. 27 Feb 2021.
Vancouver:
Yadav N. Community-affinity: measuring strength of memberships of nodes in network communities. [Internet] [Masters thesis]. University of Utah; 2015. [cited 2021 Feb 27].
Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497.
Council of Science Editors:
Yadav N. Community-affinity: measuring strength of memberships of nodes in network communities. [Masters Thesis]. University of Utah; 2015. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497

Purdue University
18.
Shanbhaq, Sunanda Vivek.
A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems.
Degree: MS, Computer and Information Technology, 2016, Purdue University
URL: http://docs.lib.purdue.edu/open_access_theses/814
► Cyber networks are complex networks with various hosts forming the entities of the network and the communication between them forming the edges of the…
(more)
▼ Cyber networks are complex networks with various hosts forming the entities of the network and the communication between them forming the edges of the network. Most cyber networks exhibit a
community structure. A
community is a group of nodes that are densely connected with each other as compared to other nodes in the network. Representing an IP network in the form of communities helps in viewing the network from different levels of granularity and makes the visualization of the network cleaner and more pleasing to the eye. This will help significantly in cyber attack
detection in large scale cyber networks. In order to serve this purpose, it is important to retrieve the
community structure fast, before the damage done by the attacker spreads and compromises the system.
This research was an effort to bring about fast
community detection of large cyber networks. The Louvain method, which is one of the most popular modularity optimization algorithms, was studied thoroughly and modified to make it faster, while preserving the quality of partitions at the same time.
Advisors/Committee Members: John Springer, John Springer, Eric J. Dietz, Eric Matson.
Subjects/Keywords: Applied sciences; Community; Community detection; Modularity; Computer Engineering; Computer Sciences
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shanbhaq, S. V. (2016). A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems. (Thesis). Purdue University. Retrieved from http://docs.lib.purdue.edu/open_access_theses/814
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shanbhaq, Sunanda Vivek. “A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems.” 2016. Thesis, Purdue University. Accessed February 27, 2021.
http://docs.lib.purdue.edu/open_access_theses/814.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shanbhaq, Sunanda Vivek. “A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems.” 2016. Web. 27 Feb 2021.
Vancouver:
Shanbhaq SV. A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems. [Internet] [Thesis]. Purdue University; 2016. [cited 2021 Feb 27].
Available from: http://docs.lib.purdue.edu/open_access_theses/814.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shanbhaq SV. A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems. [Thesis]. Purdue University; 2016. Available from: http://docs.lib.purdue.edu/open_access_theses/814
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Carnegie Mellon University
19.
Benigni, Matthew Curran.
Detection and Analysis of Online Extremist Communities.
Degree: 2017, Carnegie Mellon University
URL: http://repository.cmu.edu/dissertations/949
► Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be…
(more)
▼ Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be powerful diffusion mechanisms of both misinformation and propaganda. In some cases these groups users advocate actions or policies that could be construed as extreme along nearly any distribution of opinion, and are thus called Online Extremist Communities (OECs). Although these communities appear increasingly common, little is known about how these groups form or the methods used to influence them. The work in this thesis provides researchers a methodological framework to study these groups by answering three critical research questions: How can we detect large dynamic online activist or extremist communities? What automated tools are used to build, isolate, and influence these communities? What methods can be used to gain novel insight into large online activist or extremist communities? These group members social ties can be inferred based on the various affordances offered by OSNs for group curation. By developing heterogeneous, annotated graph representations of user behavior I can efficiently extract online activist discussion cores using an ensemble of unsupervised machine learning methods. I call this technique Ensemble Agreement Clustering. Through manual inspection, these discussion cores can then often be used as training data to detect the larger community. I present a novel supervised learning algorithm called Multiplex Vertex Classification for network bipartition on heterogeneous, annotated graphs. This methodological pipeline has also proven useful for social botnet detection, and a study of large, complex social botnets used for propaganda dissemination is provided as well. Throughout this thesis I provide Twitter case studies including communities focused on the Islamic State of Iraq and al-Sham (ISIS), the ongoing Syrian Revolution, the Euromaidan Movement in Ukraine, as well as the alt-Right.
Subjects/Keywords: Covert Network Detection; Community Detection; Annotated Networks; Multilayer Networks; Heterogeneous Networks; Spectral Clustering
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Benigni, M. C. (2017). Detection and Analysis of Online Extremist Communities. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/949
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Benigni, Matthew Curran. “Detection and Analysis of Online Extremist Communities.” 2017. Thesis, Carnegie Mellon University. Accessed February 27, 2021.
http://repository.cmu.edu/dissertations/949.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Benigni, Matthew Curran. “Detection and Analysis of Online Extremist Communities.” 2017. Web. 27 Feb 2021.
Vancouver:
Benigni MC. Detection and Analysis of Online Extremist Communities. [Internet] [Thesis]. Carnegie Mellon University; 2017. [cited 2021 Feb 27].
Available from: http://repository.cmu.edu/dissertations/949.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Benigni MC. Detection and Analysis of Online Extremist Communities. [Thesis]. Carnegie Mellon University; 2017. Available from: http://repository.cmu.edu/dissertations/949
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign
20.
Gupta, Manish.
Outlier detection for information networks.
Degree: PhD, 0112, 2013, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/44770
► The study of networks has emerged in diverse disciplines as a means of analyzing complex relationship data. There has been a significant amount of work…
(more)
▼ The study of networks has emerged in diverse disciplines as a means of analyzing complex relationship data. There has been a significant amount of work in network science which studies properties of networks, querying over networks, link analysis, influence propagation, network optimization, and many other forms of network analysis. Only recently has there been some work in the area of outlier
detection for information network data.
Outlier (or anomaly)
detection is a very broad field and has been studied in the context of a large number of application domains. Many algorithms have been proposed for outlier
detection in high-dimensional data, uncertain data, stream data and time series data. By its inherent nature, network data provides very different challenges that need to be addressed in a special way. Network data is gigantic, contains nodes of different types, rich nodes with associated attribute data, noisy attribute data, noisy link data, and is dynamically evolving in multiple ways. This thesis focuses on outlier
detection for such networks with respect to two interesting perspectives: (1)
community based outliers and (2) query based outliers.
For
community based outliers, we discuss the problem in both static as well as dynamic settings. Usually objects in a network form multiple communities. Most of the objects follow some popular
community distribution patterns and also follow similar patterns of evolution. In a static network setting, one may find some objects each of whose
community distribution does not follow any of the popular
community distribution patterns. We refer to such outliers as
Community Distribution Outliers (CDOutliers). The major challenge lies in extracting
community patterns for various object types in an integrated way. We follow an iterative two-stage approach to identify CDOutliers, which performs pattern discovery (based on a joint non-negative matrix factorization approach) and outlier
detection in a tightly integrated manner. In the dynamic setting, there are some objects which evolve in a very different way relative to other
community members, and we define such objects as temporal
community outliers. One of our studies is related to finding such outliers given two snapshots of a network (Evolutionary
Community Outliers (ECOutliers)) while the other study is more general and focuses on a setting of multiple network snapshots (
Community Trend Outliers (CTOutliers)). In both the studies, temporal patterns are discovered in an outlier-aware manner, and then outliers are discovered based on such patterns. The major challenge lies in performing cluster matching across snapshots so as to obtain temporal patterns. Another challenge is to define the outlier score once the patterns have been discovered. We propose algorithms and demonstrate the effectiveness of our algorithms in finding such outliers using both synthetic and real datasets.
The other important aspect of my research is query based outlier
detection. Given a heterogeneous network and a user subgraph query, the aim…
Advisors/Committee Members: Han, Jiawei (advisor), Han, Jiawei (Committee Chair), Zhai, ChengXiang (committee member), Abdelzaher, Tarek F. (committee member), Aggarwal, Charu C. (committee member).
Subjects/Keywords: outlier detection; Community Distribution
Outliers (CDOutliers); Evolutionary Community Outliers (ECOutliers); toread; data mining; outlier detection for graphs; outlier detection for networks; graph query processing; community detection; community outliers; anomalies; anomaly detection; evolution in networks
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gupta, M. (2013). Outlier detection for information networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44770
Chicago Manual of Style (16th Edition):
Gupta, Manish. “Outlier detection for information networks.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 27, 2021.
http://hdl.handle.net/2142/44770.
MLA Handbook (7th Edition):
Gupta, Manish. “Outlier detection for information networks.” 2013. Web. 27 Feb 2021.
Vancouver:
Gupta M. Outlier detection for information networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2142/44770.
Council of Science Editors:
Gupta M. Outlier detection for information networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44770

Australian National University
21.
Rezvani, Mojtaba.
Community Structure in Large-Scale Complex Networks
.
Degree: 2019, Australian National University
URL: http://hdl.handle.net/1885/187032
► Vertices in complex networks can be grouped into communities, where vertices inside communities are densely connected to each other and vertices from one community are…
(more)
▼ Vertices in complex networks can be grouped into communities,
where vertices inside communities are densely connected to each
other and vertices from one community are sparsely connected to
vertices in other communities. This is the so-called community
structure in complex networks. Identifying the community
structure of networks has many applications, ranging from data
mining, webpage clustering and market- ing to extracting proteins
with the same functionality in protein-protein-interaction
networks and beyond.
This thesis addresses a number of the primary problems
surrounding community structure in large-scale networks. These
problems generally revolve around two of the principal challenges
of the area, accuracy and soundness of modelling and scala-
bility to real-world networks. The problems include identifying
top-k structural hole spanners, detecting the hierarchy of
communities, detecting overlapping communi- ties, and community
search in large-scale complex networks. The thesis formally de-
fines the cohesive hierarchies of communities in complex
networks. Since scalability is a major challenge for cohesive
hierarchical community detection, the thesis incor- porates a
network sparsification technique to leverage the network size and
finds co- hesive hierarchies of communities in large-scale
complex networks. The problem of identifying top-k structural
hole spanners is formally defined in this thesis and several
scalable algorithms have been presented for this problem.
Furthermore, the thesis delves into the problem of overlapping
community detection and proposes an accu- rate fitness metric to
find overlapping communities in large-scale complex networks. The
thesis finally studies the problem of community search and
introduces a new al- gorithm for community search in complex
networks.
The thesis develops novel models, algorithms, and evaluation
measures for these problems, and presents the experimental
results of these algorithms using real-world datasets, which
outperform considerably on the scalability and accuracy of the
state of the art, in several cases.
Subjects/Keywords: complex networks;
community structure;
community detection;
community search;
overlapping community detection;
structural hole spanners;
social networks;
large-scale networks;
large-scale graphs
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rezvani, M. (2019). Community Structure in Large-Scale Complex Networks
. (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/187032
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Rezvani, Mojtaba. “Community Structure in Large-Scale Complex Networks
.” 2019. Thesis, Australian National University. Accessed February 27, 2021.
http://hdl.handle.net/1885/187032.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Rezvani, Mojtaba. “Community Structure in Large-Scale Complex Networks
.” 2019. Web. 27 Feb 2021.
Vancouver:
Rezvani M. Community Structure in Large-Scale Complex Networks
. [Internet] [Thesis]. Australian National University; 2019. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/1885/187032.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Rezvani M. Community Structure in Large-Scale Complex Networks
. [Thesis]. Australian National University; 2019. Available from: http://hdl.handle.net/1885/187032
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
22.
Rabbany khorasgani, Reihaneh.
Modular Structure of Complex Networks.
Degree: PhD, Department of Computing Science, 2016, University of Alberta
URL: https://era.library.ualberta.ca/files/cxk81jk64k
► Complex networks represent the relationships or interactions between entities in a complex system, such as biological interactions between proteins and genes, hyperlinks between web pages,…
(more)
▼ Complex networks represent the relationships or
interactions between entities in a complex system, such as
biological interactions between proteins and genes, hyperlinks
between web pages, co-authorships between research scholars.
Although drawn from a wide range of domains, real-world networks
exhibit similar structural properties and evolution patterns. A
fundamental property of these networks is their tendency to
organize according to an underlying modular structure, commonly
referred to as clustering or community structure. This thesis
focuses on comparing, quantifying, modeling, and utilizing this
common structure in real-world networks. First, it presents
generalizations of well-established traditional clustering criteria
and propose proper adaptations to make them applicable in the
context of networks. This includes generalizations and extensions
of 1) the well-known clustering validity criteria that quantify the
goodness of a single clustering; and 2) clustering agreement
measures that compare two clusterings of the same dataset. The
former introduces a new set of measures for quantifying the
goodness of a candid community structure, while the latter
establishes a new family of clustering distances suitable for
comparing two possible community structures of a given network.
These adapted measures are useful in both defining and evaluating
the communities in networks. Second, it discusses generative
network models and introduces an intuitive and flexible model for
synthesizing modular networks that closely comply with the
characteristics observed for real-world networks. This network
synthesizer is particularly useful for generating benchmark
datasets with built-in modular structure, which are used in
evaluation of community detection algorithms. Lastly, it
investigates how the modular structure of networks can be utilized
in different contexts. In particular, it focuses on an e-learning
case study, where the network modules can effectively outline the
collaboration groups of students, as well as the topics of their
discussions; which is used to monitor the participation trends of
students throughout an online course. Then, it examines the
interplay between the attributes of nodes and their memberships in
modules, and present how this interplay can be leveraged for
predicting (missing) attribute values; where alternative modular
structures are derived, each in better alignment with a given
attribute.
Subjects/Keywords: Complex Networks; Community Detection; Community Evaluation; Network Models; Clustering Agreement; Clustering Networks; Attributed Graphs
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rabbany khorasgani, R. (2016). Modular Structure of Complex Networks. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cxk81jk64k
Chicago Manual of Style (16th Edition):
Rabbany khorasgani, Reihaneh. “Modular Structure of Complex Networks.” 2016. Doctoral Dissertation, University of Alberta. Accessed February 27, 2021.
https://era.library.ualberta.ca/files/cxk81jk64k.
MLA Handbook (7th Edition):
Rabbany khorasgani, Reihaneh. “Modular Structure of Complex Networks.” 2016. Web. 27 Feb 2021.
Vancouver:
Rabbany khorasgani R. Modular Structure of Complex Networks. [Internet] [Doctoral dissertation]. University of Alberta; 2016. [cited 2021 Feb 27].
Available from: https://era.library.ualberta.ca/files/cxk81jk64k.
Council of Science Editors:
Rabbany khorasgani R. Modular Structure of Complex Networks. [Doctoral Dissertation]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cxk81jk64k

Purdue University
23.
Bang, Seokhun.
Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.
Degree: MSIE, Industrial Engineering, 2015, Purdue University
URL: https://docs.lib.purdue.edu/open_access_theses/1046
► Graph clustering is a field of study that helps reveal characteristics of communities. Systems can be viewed as networks and form communities in various areas…
(more)
▼ Graph clustering is a field of study that helps reveal characteristics of communities. Systems can be viewed as networks and form communities in various areas such as biology, computer science, engineering, economics, and politics. A clustering algorithm is a tool that detects communities and it can be also considered as a pre-processing step to study the characteristics of detected communities. Many efforts were made to develop a well performing clustering algorithm in different types of networks. In recent literature, a concept of multi-layer graphs emerged, and clustering algorithms are being developed to detect communities in the multi-layer graphs. In this thesis, we propose a clustering algorithm that can be applied to both single-layer and multi-layer graphs. We test the algorithm on simulated data and real data in both single-layer and multi-layer graphs. Four performance measures were used to evaluate the performance of the proposed algorithm. We also study how the performance measures are correlated with each other and what the effects of parameter, presented in the proposed algorithm are. The thesis concludes with summary of research findings and directions of the future research.
Advisors/Committee Members: Seokcheon Lee, Hyonho Chun, Mark R Lehto.
Subjects/Keywords: Clustering algorithm; Community detection; Community structure; Label Propagation Method; LabelRank; Markov Cluster Algorithm
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bang, S. (2015). Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. (Thesis). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_theses/1046
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Thesis, Purdue University. Accessed February 27, 2021.
https://docs.lib.purdue.edu/open_access_theses/1046.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Web. 27 Feb 2021.
Vancouver:
Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Internet] [Thesis]. Purdue University; 2015. [cited 2021 Feb 27].
Available from: https://docs.lib.purdue.edu/open_access_theses/1046.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Thesis]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_theses/1046
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Purdue University
24.
Bang, Seokhun.
Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.
Degree: MSIE, Industrial Engineering, 2015, Purdue University
URL: https://docs.lib.purdue.edu/open_access_theses/1037
► Graph clustering is a field of study that helps reveal characteristics of communities. Systems can be viewed as networks and form communities in various…
(more)
▼ Graph clustering is a field of study that helps reveal characteristics of communities. Systems can be viewed as networks and form communities in various areas such as biology, computer science, engineering, economics, and politics. A clustering algorithm is a tool that detects communities and it can be also considered as a pre-processing step to study the characteristics of detected communities. Many efforts were made to develop a well performing clustering algorithm in different types of networks. In recent literature, a concept of multi-layer graphs emerged, and clustering algorithms are being developed to detect communities in the multi-layer graphs. In this thesis, we propose a clustering algorithm that can be applied to both single-layer and multi-layer graphs. We test the algorithm on simulated data and real data in both single-layer and multi-layer graphs. Four performance measures were used to evaluate the performance of the proposed algorithm. We also study how the performance measures are correlated with each other and what the effects of parameter, presented in the proposed algorithm are. The thesis concludes with summary of research findings and directions of the future research.
Advisors/Committee Members: Seokcheon Lee, Hyonho Chun, Mark R Lehto.
Subjects/Keywords: Clustering algorithm; Community detection; Community structure; Label Propagation Method; LabelRank; Markov Cluster Algorithm
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bang, S. (2015). Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. (Thesis). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_theses/1037
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Thesis, Purdue University. Accessed February 27, 2021.
https://docs.lib.purdue.edu/open_access_theses/1037.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Web. 27 Feb 2021.
Vancouver:
Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Internet] [Thesis]. Purdue University; 2015. [cited 2021 Feb 27].
Available from: https://docs.lib.purdue.edu/open_access_theses/1037.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Thesis]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_theses/1037
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology
25.
Jiang, Xuehan (author).
A community-evolution based approach for detecting the echo chamber effect in recommender systems.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:ee83befe-cabf-46a8-af82-7f528c895a52
► Information systems, such as information retrieval machines and recommendation systems, utilize various user information and history behaviors to provide personalized content to users. However, a…
(more)
▼ Information systems, such as information retrieval machines and recommendation systems, utilize various user information and history behaviors to provide personalized content to users. However, a debate on whether the personalization in information systems can trigger the online echo chamber effect has emerged. The online echo chamber effect describes the situation that Internet users are segregated into groups based on common interests or opinions and their existing views or confirmation bias are reinforced by repetition. Based on the idea that the strong community structure of the user network suggests the emergence of the echo chamber effect, we propose a brand new methodology based on temporal community evolution to detect the echo chamber effect in recommender systems. A two-layer temporal user network is constructed, with the first layer representing the user taste similarity and the second layer encoding potential information flows between users. Then, we apply an estrangement confinement based algorithm to detect the temporal communities in the two-layer temporal network. Our experiment results on the MovieLens dataset suggest the emergence of the echo chamber effect. Moreover, we find that the echo chamber effect is becoming more remarkable over time. In addition, we observe that some users tend to stay in one community over time. These users are potentially affected by the echo chamber effect and have a higher mean node strength in both network layers.
Computer Science
Advisors/Committee Members: Wang, Huijuan (mentor), Hanjalic, Alan (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Recommender Systems; Echo Chamber Effect; Temporal Community Detection; Community Evolution; User Segregation
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jiang, X. (. (2018). A community-evolution based approach for detecting the echo chamber effect in recommender systems. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ee83befe-cabf-46a8-af82-7f528c895a52
Chicago Manual of Style (16th Edition):
Jiang, Xuehan (author). “A community-evolution based approach for detecting the echo chamber effect in recommender systems.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021.
http://resolver.tudelft.nl/uuid:ee83befe-cabf-46a8-af82-7f528c895a52.
MLA Handbook (7th Edition):
Jiang, Xuehan (author). “A community-evolution based approach for detecting the echo chamber effect in recommender systems.” 2018. Web. 27 Feb 2021.
Vancouver:
Jiang X(. A community-evolution based approach for detecting the echo chamber effect in recommender systems. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27].
Available from: http://resolver.tudelft.nl/uuid:ee83befe-cabf-46a8-af82-7f528c895a52.
Council of Science Editors:
Jiang X(. A community-evolution based approach for detecting the echo chamber effect in recommender systems. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:ee83befe-cabf-46a8-af82-7f528c895a52

UCLA
26.
Razaee, Zahra.
Community Detection in Networks with Node Covariates.
Degree: Statistics, 2017, UCLA
URL: http://www.escholarship.org/uc/item/3343v33s
► Community detection or clustering is a fundamental task in the analysis of network data. Most networks come with annotations which can be in form of…
(more)
▼ Community detection or clustering is a fundamental task in the analysis of network data. Most networks come with annotations which can be in form of node covariates such as a person's age, gender and location and/or edge covariates such as time stamps and ratings. However, most of the existing community detection approaches infer the community memberships merely based on the network structure. Moreover, many real networks have a bipartite structure which makes community detection challenging. In this dissertation, we first propose a model-based approach which allows for matched communities in the bipartite setting, in addition to node covariates with information about the matching. We derive a simple fast algorithm for fitting the model, based on variational inference ideas. A variation of the model to allow for degree-correction is also considered, in addition to a novel approach to fitting such degree-correctedmodels. We also propose a unified affinity matrix (USim) to leverage the node covariates information that can be used in unipartite networks (directed and undirected) as well as the bipartite networks that combines the information from the network with that from the node covariates into a single similarity matrix, which can then be input to a spectral clustering algorithm.We show the effectiveness of both approaches on simulated and real data, namely, page-user networkscollected from Wikipedia.
Subjects/Keywords: Statistics; Bipartite; Community Detection; Network; Node Covariates; Stochastic Blockmodel
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Razaee, Z. (2017). Community Detection in Networks with Node Covariates. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/3343v33s
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Razaee, Zahra. “Community Detection in Networks with Node Covariates.” 2017. Thesis, UCLA. Accessed February 27, 2021.
http://www.escholarship.org/uc/item/3343v33s.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Razaee, Zahra. “Community Detection in Networks with Node Covariates.” 2017. Web. 27 Feb 2021.
Vancouver:
Razaee Z. Community Detection in Networks with Node Covariates. [Internet] [Thesis]. UCLA; 2017. [cited 2021 Feb 27].
Available from: http://www.escholarship.org/uc/item/3343v33s.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Razaee Z. Community Detection in Networks with Node Covariates. [Thesis]. UCLA; 2017. Available from: http://www.escholarship.org/uc/item/3343v33s
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley
27.
Bhattacharyya, Sharmodeep.
A Study of High-dimensional Clustering and Statistical Inference on Networks.
Degree: Statistics, 2013, University of California – Berkeley
URL: http://www.escholarship.org/uc/item/9sx0k48k
► Clustering is an important unsupervised classification technique. In supervised classification, we are provided with a collection of labeled (pre-classified) patterns and the problem is to…
(more)
▼ Clustering is an important unsupervised classification technique. In supervised classification, we are provided with a collection of labeled (pre-classified) patterns and the problem is to label a newly encountered, yet unlabeled, pattern. At first, we consider clustering in Euclidean space in large dimensions. Then, we delve into the discrete setting of networks. We go into the issues related to network modeling and then into a specific method of clustering in networks. In the first chapter, we consider the problem of estimation and deriving theoretical properties of the estimators for the elliptical distributions. The class of elliptical distributions have distributions with varied tail behavior. So, estimation under class of elliptic distributions lead to automatic robust estimators. The goal of the chapter is to propose efficient and adaptive regularized estimators for the nonparametric component, mean and covariance matrix of the elliptical distributions in both high and fixed dimensional situations. An algorithm for regularized estimation of mixture of elliptical distributions will also lead to an algorithm for finding elliptical clusters in high dimensional space and such an approach is also given in the chapter. In clustering, one of the main challenges is the detection of number of clusters. Most clustering algorithms need the number of clusters to be specified beforehand. In chapter two, we propose a new method of selecting number of clusters, based on hypothesis testing. The study of networks has received increased attention recently not only from the social sciences and statistics but also from physicists,computer scientists and mathematicians. But a proper statistical analysis of features of different stochastic models of networks is still underway. In chapter three, we give an account of different network models and then we analyze a specific nonparametric model for networks. We consider the nonparametric estimate of link probabilities in dense social graphs in the context of network modeling and exploratory statistics.In chapter four, we also propose bootstrap methods for finding empirical distribution of count features or `moments' and smooth functions of these for the networks. Using these methods, we can not only estimate variance of count features but also get good estimates of such feature counts, which are usually expensive to compute numerically in large networks. In our paper, we prove theoretical properties of the bootstrap variance estimates of the count features as well as show their efficacy through simulation. We also use the method on publicly available Facebook network data for estimate of variance and expectation of some count features. In chapter five, we propose a clustering or community detection scheme for networks. One of the principal problem in networks is community detection. Many algorithms have been proposed for community finding but most of them do not have have theoretical guarantee for sparse networks and networks close to phase transition boundary proposed by…
Subjects/Keywords: Statistics; Bootstrap; Clustering; Community detection; Elliptical distributions; High-dimensional inference; Networks
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bhattacharyya, S. (2013). A Study of High-dimensional Clustering and Statistical Inference on Networks. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9sx0k48k
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bhattacharyya, Sharmodeep. “A Study of High-dimensional Clustering and Statistical Inference on Networks.” 2013. Thesis, University of California – Berkeley. Accessed February 27, 2021.
http://www.escholarship.org/uc/item/9sx0k48k.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bhattacharyya, Sharmodeep. “A Study of High-dimensional Clustering and Statistical Inference on Networks.” 2013. Web. 27 Feb 2021.
Vancouver:
Bhattacharyya S. A Study of High-dimensional Clustering and Statistical Inference on Networks. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2021 Feb 27].
Available from: http://www.escholarship.org/uc/item/9sx0k48k.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bhattacharyya S. A Study of High-dimensional Clustering and Statistical Inference on Networks. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/9sx0k48k
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

San Jose State University
28.
Murali, Mrudula.
Online Local Communities.
Degree: MS, Computer Science, 2019, San Jose State University
URL: https://doi.org/10.31979/etd.4cu5-yv9s
;
https://scholarworks.sjsu.edu/etd_projects/723
► A community in a network is a group of nodes that are densely and closely connected to each other, get sparsely connected to the…
(more)
▼ A
community in a network is a group of nodes that are densely and closely connected to each other, get sparsely connected to the nodes outside the
community. Finding communities in a large network helps solve many real-world problems. But detecting such communities in a complex network by focusing on the whole network is not feasible. Instead, we focus on finding communities around one or more seed node(s) of interest. Therefore, in this project, we find local communities. Moreover, we consider the online setting where the whole graph is unknown in the beginning and we get a stream of edges, i.e., pair of nodes, or a stream of higher order structures, i.e., triangles of nodes.
We created a new dataset that consists of web pages and their links by using the Internet Archive. We extended an existing online local graph
community detection algorithm, called COEUS, for higher order structures such as triangles of nodes. We provide experimental results and comparison of the existing method and our proposed method using two public datasets, the Amazon and the DBLP as well as for our new Webpages dataset. In the experimental results, we see that the proposed method performs better than the existing method for one out of three test cases for the public dataset but not for our Webpages dataset. This is because the Webpages dataset has a large number of nodes with degree 1 which poses a problem for modified COEUS because it takes triangles as an input stream.
Advisors/Committee Members: Katerina Potika, Christopher Pollett, Sami Khuri.
Subjects/Keywords: Community detection; Local graph clustering; Online com- munity; Theory and Algorithms
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Murali, M. (2019). Online Local Communities. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.4cu5-yv9s ; https://scholarworks.sjsu.edu/etd_projects/723
Chicago Manual of Style (16th Edition):
Murali, Mrudula. “Online Local Communities.” 2019. Masters Thesis, San Jose State University. Accessed February 27, 2021.
https://doi.org/10.31979/etd.4cu5-yv9s ; https://scholarworks.sjsu.edu/etd_projects/723.
MLA Handbook (7th Edition):
Murali, Mrudula. “Online Local Communities.” 2019. Web. 27 Feb 2021.
Vancouver:
Murali M. Online Local Communities. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 Feb 27].
Available from: https://doi.org/10.31979/etd.4cu5-yv9s ; https://scholarworks.sjsu.edu/etd_projects/723.
Council of Science Editors:
Murali M. Online Local Communities. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.4cu5-yv9s ; https://scholarworks.sjsu.edu/etd_projects/723

University of Michigan
29.
Karrer, Brian C.
Topics in Networks: Community Detection, Random Graphs, and Network Epidemiology.
Degree: PhD, Physics, 2012, University of Michigan
URL: http://hdl.handle.net/2027.42/91512
► In this dissertation, we present research on several topics in networks including community detection, random graphs, and network epidemiology. Traditional stochastic blockmodels may produce inaccurate…
(more)
▼ In this dissertation, we present research on several topics in networks including
community detection, random graphs, and network epidemiology.
Traditional stochastic blockmodels may produce inaccurate fits to complex networks with heterogeneous degree distributions and we devise a degree-corrected blockmodel that alleviates this problematic behavior. The resulting objective function for
community detection using the degree-corrected version outperforms the traditional model at finding communities on a variety of real-world and synthetic tests. Then we study a different generative model that associates communities to the edges of the network and naturally includes overlapping vertex communities. We create a fast and accurate algorithm to fit this model to empirical networks and show that it can be used to quickly find non-overlapping communities as well.
We also develop random graph models for directed acyclic graphs, a class of networks including family trees and citation networks. We argue that the lack of cycles comes from an ordering constraint and then generalize the configuration model to incorporate this constraint. We calculate many properties of these models and demonstrate that some of the model predictions agree quite well with real-world networks, emphasizing the importance of vertex ordering to generating directed acyclic networks with realistic properties.
Finally, we examine the spread of disease over networks, starting with a simple model of two diseases spreading with cross-immunity, where infection by one disease makes an individual immune to the other disease and vice versa. Utilizing a timescale separation argument, we map the system to consecutive bond percolation, one disease spreading after the other. The resulting phase diagram includes discontinuous and continuous phase transitions and a coexistence region where both diseases can spread to a substantial fraction of the population. Then we analyze a flexible susceptible-infected-recovered model that allows arbitrary timing for recovery and infection instead of the traditional exponential distributions. Using a message passing approach, we derive the exact expected behavior for trees and random graphs in the large graph size limit, and show that these results are bounds on other networks.
Advisors/Committee Members: Newman, Mark E. (committee member), Deegan, Robert David (committee member), Doering, Charles R. (committee member), Sander, Leonard M. (committee member), Ziff, Robert M. (committee member).
Subjects/Keywords: Networks; Community Detection; Random Graphs; Network Epidemiology; Physics; Science
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Karrer, B. C. (2012). Topics in Networks: Community Detection, Random Graphs, and Network Epidemiology. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/91512
Chicago Manual of Style (16th Edition):
Karrer, Brian C. “Topics in Networks: Community Detection, Random Graphs, and Network Epidemiology.” 2012. Doctoral Dissertation, University of Michigan. Accessed February 27, 2021.
http://hdl.handle.net/2027.42/91512.
MLA Handbook (7th Edition):
Karrer, Brian C. “Topics in Networks: Community Detection, Random Graphs, and Network Epidemiology.” 2012. Web. 27 Feb 2021.
Vancouver:
Karrer BC. Topics in Networks: Community Detection, Random Graphs, and Network Epidemiology. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/2027.42/91512.
Council of Science Editors:
Karrer BC. Topics in Networks: Community Detection, Random Graphs, and Network Epidemiology. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/91512

Penn State University
30.
Ghurye, Akshay Dattatraya.
DETECTION AND EVALUATION OF COMMUNITY STRUCTURES IN SOCIAL NETWORKS
.
Degree: 2011, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/12603
► In today's world, social media networks capture interactions among people through comments on blogs, posts and feeds. The public availability of these networks has allowed…
(more)
▼ In today's world, social media networks capture interactions among people through comments on blogs, posts and feeds. The public availability of these networks has allowed researchers and businesses alike to delve more into these preferences so as to extract communities which clearly define their formation. In social networks, people tend to have more than one preference over different products which makes it difficult to put them in a single
community. Although
community detection has been well applied to social networks, not much work has been done in detecting overlapping communities within these networks. In this paper we describe an algorithm which applies a game theoretic approach to graph clustering to determine overlapping communities within complex networks and also show how a parallel implementation of the algorithm can be used to detect communities in lesser time than its previous implementations. Further we run the algorithm on various social networks to detect overlapping communities and propose a method to analyze them once they are determined. We conclude by providing impetus on the running time of this algorithm and expressing the need for faster algorithms to detect and analyze social media networks.
Advisors/Committee Members: Soundar Kumara, Thesis Advisor/Co-Advisor, Soundar R. T. Kumara, Thesis Advisor/Co-Advisor.
Subjects/Keywords: parallel programming; community detection; overlapping; social networks; complexity
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ghurye, A. D. (2011). DETECTION AND EVALUATION OF COMMUNITY STRUCTURES IN SOCIAL NETWORKS
. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/12603
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ghurye, Akshay Dattatraya. “DETECTION AND EVALUATION OF COMMUNITY STRUCTURES IN SOCIAL NETWORKS
.” 2011. Thesis, Penn State University. Accessed February 27, 2021.
https://submit-etda.libraries.psu.edu/catalog/12603.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ghurye, Akshay Dattatraya. “DETECTION AND EVALUATION OF COMMUNITY STRUCTURES IN SOCIAL NETWORKS
.” 2011. Web. 27 Feb 2021.
Vancouver:
Ghurye AD. DETECTION AND EVALUATION OF COMMUNITY STRUCTURES IN SOCIAL NETWORKS
. [Internet] [Thesis]. Penn State University; 2011. [cited 2021 Feb 27].
Available from: https://submit-etda.libraries.psu.edu/catalog/12603.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ghurye AD. DETECTION AND EVALUATION OF COMMUNITY STRUCTURES IN SOCIAL NETWORKS
. [Thesis]. Penn State University; 2011. Available from: https://submit-etda.libraries.psu.edu/catalog/12603
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
◁ [1] [2] [3] [4] [5] [6] [7] [8] ▶
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