You searched for subject:(Complex networks).
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University of Minnesota
1.
Fuller, Samantha.
Second Order Networks with spatial structure.
Degree: MS, Mathematics, 2016, University of Minnesota
URL: http://hdl.handle.net/11299/182129 
► Synchronization of spiking activity across neurons plays a role in many processes in the brain. Using the framework of Second Order Networks (SONETs) paired with…
(more)
▼ Synchronization of spiking activity across neurons plays a role in many processes in the brain. Using the framework of Second Order Networks (SONETs) paired with a global ring structure, we looked at the relationships between the connectivity statistics and two key eigenvalue quantities related to the synchrony of the network - the largest eigenvalue of the connectivity matrix and the variance of the eigenvalues of the Laplacian. Previously, Zhao et al. (2011) examined these relationships in the case of homogeneous SONETs, in which there is no spatial variation in the network. In this work, we broaden our view to SONETs where we allow the connection probabilities to depend on the spatial structure of the network. First, we develop an algorithm to generate SONETs which allows us to specify both the global and local geometry of the network. We then randomly generated a wide range of SONETs to examine the relationships between the connectivity statistics and the eigenvalue quantities of the resulting networks. We find that two of the second order statistics, namely those corresponding to the frequency of convergent connections and to the frequency of chain connections, primarily influence the values of the two eigenvalue quantities. Our results are remarkably similar to those of the homogeneous case, indicating that the qualitative relationship we see between synchrony and second order statstics should extend to a larger class of networks. We also find that for the networks we considered, the parameters used to describe the overall geometry of the network had a minimal influence on the two key eigenvalue quantities.
Subjects/Keywords: complex networks; neuronal networks; synchrony
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APA (6th Edition):
Fuller, S. (2016). Second Order Networks with spatial structure. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/182129
Chicago Manual of Style (16th Edition):
Fuller, Samantha. “Second Order Networks with spatial structure.” 2016. Masters Thesis, University of Minnesota. Accessed December 14, 2019.
http://hdl.handle.net/11299/182129.
MLA Handbook (7th Edition):
Fuller, Samantha. “Second Order Networks with spatial structure.” 2016. Web. 14 Dec 2019.
Vancouver:
Fuller S. Second Order Networks with spatial structure. [Internet] [Masters thesis]. University of Minnesota; 2016. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/11299/182129.
Council of Science Editors:
Fuller S. Second Order Networks with spatial structure. [Masters Thesis]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/182129
University of Pretoria
2.
Meintjes, Sumarie.
Analysing
network motifs in a complex network of freight
movements.
Degree: Industrial and Systems
Engineering, 2016, University of Pretoria
URL: http://hdl.handle.net/2263/51895
► Motifs are over-represented subgraphs in a complex network, and represent the building blocks of the network. There is a lack of studies that apply complex…
(more)
▼ Motifs are over-represented subgraphs in a
complex
network, and represent the building blocks of the network. There is
a lack of studies that apply
complex network theory in a supply
chain context. In this dissertation 3-node motifs were identified
and analysed in a
complex network representing direct freight trips
between firms in the Nelson Mandela Bay Metropolitan, South Africa.
The G-Tries and ISMAGS algorithms were tested on small
complex
networks, and were compared according to quantitative and
qualitative properties. It was found that ISMAGS is the most
suitable for this dissertation. Freight activities were identified
from raw GPS traces of freight vehicles, and the activities were
clustered into firms using a density-based clustering algorithm.
Multi-objective optimisation indicated that the clustering
parameter configuration
γ = (20, 20) can be used to increase the
visual accuracy of the firms, while maximising the completeness of
the
complex network. The freight
complex network was built by
identifying direct trips between firms. Using ISMAGS, it was found
that three firms with two (X0X) or three (XXX) reciprocal freight
trips between them are statistically overrepresented in the
network. A brewery, shopping centres, distribution centres, and
truck stops frequently appeared in the motifs. Motifs that contain
the brewery and one of the truck stops were identified as the most
central motifs based on the number of direct freight trips that
occur in the motifs. Some freight trips in XXX motifs occur
frequently over long distances, increasing total transport costs of
the firms. Supply chain improvements can be applied to these
identified firms. It was also found that there is a relationship
between ranking firms according to the number of motifs they appear
in and their degree centrality scores. This relationship can be
studied more rigorously in future work. Another avenue for future
research is to study the supply chain structures of firms in
motifs, as well as the commodity flows between firms in
motifs.
Advisors/Committee Members: Joubert, Johan W. (advisor).
Subjects/Keywords: Complex
networks; UCTD
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APA (6th Edition):
Meintjes, S. (2016). Analysing
network motifs in a complex network of freight
movements. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/51895
Chicago Manual of Style (16th Edition):
Meintjes, Sumarie. “Analysing
network motifs in a complex network of freight
movements.” 2016. Masters Thesis, University of Pretoria. Accessed December 14, 2019.
http://hdl.handle.net/2263/51895.
MLA Handbook (7th Edition):
Meintjes, Sumarie. “Analysing
network motifs in a complex network of freight
movements.” 2016. Web. 14 Dec 2019.
Vancouver:
Meintjes S. Analysing
network motifs in a complex network of freight
movements. [Internet] [Masters thesis]. University of Pretoria; 2016. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/2263/51895.
Council of Science Editors:
Meintjes S. Analysing
network motifs in a complex network of freight
movements. [Masters Thesis]. University of Pretoria; 2016. Available from: http://hdl.handle.net/2263/51895
University of Sydney
3.
Thedchanamoorthy, Gnanakumar.
New approaches and their applications in measuring mixing patterns of complex networks
.
Degree: 2014, University of Sydney
URL: http://hdl.handle.net/2123/13211
► In this thesis, mixing patterns of complex networks are analysed. Synthesised canonical networks, scale-free networks, small-world networks and random networks along with existing, real-world networks…
(more)
▼ In this thesis, mixing patterns of complex networks are analysed. Synthesised canonical networks, scale-free networks, small-world networks and random networks along with existing, real-world networks are analysed using various approaches. Assortativity is a measure that quantifies the similarity among nodes that are connected. In this thesis, two new approaches to quantify node assortativity have been proposed. First approach presented eliminates the dependency of node assortativity calculation on average excess degree, which was present in currently used approache. The second approach to node assortativity proposed is calculated based on the contribution of nodes toward the network assortativity. Similarly, a new approach to quantify the heterogeneity of nodes' neighbors has been proposed. It is shown that standard deviations of degree differences between nodes could be used to quantify the heterogeneity of nodes. This measure, which is called ‘versatility’ in this thesis, is then used to classify networks and used to identify the impact of versatility on other measures of networks. Using versatility calculations, it was found that there are three classes of real world networks: (i) Networks where the versatility converges to a non-zero value with node degrees (ii) Networks where the versatility converges to zero with node degrees (iii) Networks where the versatility does not converge with degree. Also, two cases were identified - a) Networks where the majority of the nodes have low versatility values, and b) Networks where the majority of the nodes have medium versatility values. It was found that often (i) and (ii) correlate with (a) and (iii) correlates with (b). Another measure called ‘Area Under Curve’, to quantify the level of herd-immunity present in a network is also introduced. Using this measure, it is shown that assortative networks exhibited higher levels of herd immunity.
Subjects/Keywords: Assortativity;
Complex networks
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APA (6th Edition):
Thedchanamoorthy, G. (2014). New approaches and their applications in measuring mixing patterns of complex networks
. (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/13211
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):
Thedchanamoorthy, Gnanakumar. “New approaches and their applications in measuring mixing patterns of complex networks
.” 2014. Thesis, University of Sydney. Accessed December 14, 2019.
http://hdl.handle.net/2123/13211.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Thedchanamoorthy, Gnanakumar. “New approaches and their applications in measuring mixing patterns of complex networks
.” 2014. Web. 14 Dec 2019.
Vancouver:
Thedchanamoorthy G. New approaches and their applications in measuring mixing patterns of complex networks
. [Internet] [Thesis]. University of Sydney; 2014. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/2123/13211.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Thedchanamoorthy G. New approaches and their applications in measuring mixing patterns of complex networks
. [Thesis]. University of Sydney; 2014. Available from: http://hdl.handle.net/2123/13211
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Universidade Nova
4.
Zanin, Massimiliano.
Complex networks and data mining: toward a new perspective for the understanding of complex systems.
Degree: 2015, Universidade Nova
URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14064
► Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last…
(more)
▼ Complex systems, i.e. systems composed of a large set of elements interacting in a
non-linear way, are constantly found all around us. In the last decades, different approaches
have been proposed toward their understanding, one of the most interesting
being the
Complex Network perspective. This legacy of the 18th century mathematical
concepts proposed by Leonhard Euler is still current, and more and more relevant in
real-world problems. In recent years, it has been demonstrated that network-based representations
can yield relevant knowledge about
complex systems. In spite of that, several
problems have been detected, mainly related to the degree of subjectivity involved
in the creation and evaluation of such network structures. In this Thesis, we propose addressing
these problems by means of different data mining techniques, thus obtaining a
novel hybrid approximation intermingling
complex networks and data mining. Results
indicate that such techniques can be effectively used to i) enable the creation of novel network
representations, ii) reduce the dimensionality of analyzed systems by pre-selecting
the most important elements, iii) describe
complex networks, and iv) assist in the analysis
of different network topologies. The soundness of such approach is validated through
different validation cases drawn from actual biomedical problems, e.g. the diagnosis of
cancer from tissue analysis, or the study of the dynamics of the brain under different
neurological disorders.
Advisors/Committee Members: Sousa, Pedro, Boccaletti, Stefano.
Subjects/Keywords: Complex systems; Complex networks; Data mining
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APA ·
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APA (6th Edition):
Zanin, M. (2015). Complex networks and data mining: toward a new perspective for the understanding of complex systems. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14064
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):
Zanin, Massimiliano. “Complex networks and data mining: toward a new perspective for the understanding of complex systems.” 2015. Thesis, Universidade Nova. Accessed December 14, 2019.
http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14064.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zanin, Massimiliano. “Complex networks and data mining: toward a new perspective for the understanding of complex systems.” 2015. Web. 14 Dec 2019.
Vancouver:
Zanin M. Complex networks and data mining: toward a new perspective for the understanding of complex systems. [Internet] [Thesis]. Universidade Nova; 2015. [cited 2019 Dec 14].
Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14064.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zanin M. Complex networks and data mining: toward a new perspective for the understanding of complex systems. [Thesis]. Universidade Nova; 2015. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/14064
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Boston University
5.
Dehmamy, Nima.
First principles and effective theory approaches to dynamics of complex networks.
Degree: PhD, Physics, 2016, Boston University
URL: http://hdl.handle.net/2144/14523
► This dissertation concerns modeling two aspects of dynamics of complex networks: (1) response dynamics and (2) growth and formation. A particularly challenging class of networks…
(more)
▼ This dissertation concerns modeling two aspects of dynamics of complex networks: (1)
response dynamics and (2) growth and formation.
A particularly challenging class of networks are ones in which both nodes and links are
evolving over time – the most prominent example is a financial network. In the first part
of the dissertation we present a model for the response dynamics in networks near a metastable
point. We start with a Landau-Ginzburg approach and show that the most general
lowest order Lagrangians for dynamical weighted networks can be used to derive conditions
for stability under external shocks. Using a closely related model, which is easier to solve
numerically, we propose a powerful and intuitive set of equations for response dynamics
of financial networks. We find the stability conditions of the model and find two phases:
“calm” phase , in which changes are sub-exponential and where the system moves to a new,
close-by equilibrium; “frantic” phase, where changes are exponential, with negative blows
resulting in crashes and positive ones leading to formation of "bubbles". We empirically
verify these claims by analyzing data from Eurozone crisis of 2009-2012 and stock markets.
We show that the model correctly identifies the time-line of the Eurozone crisis, and in the stock market data it correctly reproduces the auto-correlations and phases observed in the
data.
The second half of the dissertation addresses the following question: Do networks that
form due to local interactions (local in real space, or in an abstract parameter space) have
characteristics different from networks formed of random or non-local interactions? Using
interacting fields obeying Fokker-Planck equations we show that many network characteristics
such as degree distribution, degree-degree correlation and clustering can either be
derived analytically or there are analytical bounds on their behaviour. In particular, we
derive recursive equations for all powers of the ensemble average of the adjacency matrix.
We analyze a few real world networks and show that some networks that seem to form from
local interactions indeed have characteristics almost identical to simulations based on our
model, in contrast with many other networks.
Subjects/Keywords: Physics; Complex networks; Complex systems; Effective theory
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APA ·
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MLA ·
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Manager
APA (6th Edition):
Dehmamy, N. (2016). First principles and effective theory approaches to dynamics of complex networks. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/14523
Chicago Manual of Style (16th Edition):
Dehmamy, Nima. “First principles and effective theory approaches to dynamics of complex networks.” 2016. Doctoral Dissertation, Boston University. Accessed December 14, 2019.
http://hdl.handle.net/2144/14523.
MLA Handbook (7th Edition):
Dehmamy, Nima. “First principles and effective theory approaches to dynamics of complex networks.” 2016. Web. 14 Dec 2019.
Vancouver:
Dehmamy N. First principles and effective theory approaches to dynamics of complex networks. [Internet] [Doctoral dissertation]. Boston University; 2016. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/2144/14523.
Council of Science Editors:
Dehmamy N. First principles and effective theory approaches to dynamics of complex networks. [Doctoral Dissertation]. Boston University; 2016. Available from: http://hdl.handle.net/2144/14523
University of Hawaii – Manoa
6.
Altunkaya, Ali.
Multiscale community detection using markov dynamics.
Degree: 2015, University of Hawaii – Manoa
URL: http://hdl.handle.net/10125/100263
► M.S. University of Hawaii at Manoa 2014.
Complex networks is an interdisciplinary research area getting attention from a variety of disciplines including sociology, biology, and…
(more)
▼ M.S. University of Hawaii at Manoa 2014.
Complex networks is an interdisciplinary research area getting attention from a variety of disciplines including sociology, biology, and computer science. It studies the properties of complex systems that may have many functional or structural subunits. Community detection algorithms are one of the major approaches to analyse complex networks by finding these intermediate-level subunits called modules or communities. Furthermore, some networks may have multilevel or overlapping community structures.
InfoMap is one of the best performing algorithms that finds the communities of a network by compressing the flow of information [25, 17]. In this work, we introduced Markov Dynamics to the InfoMap in order to detect communities at multiscales. Although Markov Dynamics have been applied to the InfoMap for undirected networks before [28], this was the first application of Markov Dynamics to the InfoMap for directed networks. Additionally, we added a feature to detect overlapping nodes using the compression of flow on the boundary nodes, similar to the approach described in [9] before.
These two features were combined into the InfoMap for directed networks. We evaluated these two features on synthetic networks and benchmark graphs. We have comparable results with the Hierarchical InfoMap [26] for multilevel community detection, but improvement in runtime is needed. We evaluated the overlapping feature by its own ability, and found that it can detect overlapping nodes for networks with sparse overlaps.
Subjects/Keywords: Markov dynamics; Complex networks
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APA ·
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APA (6th Edition):
Altunkaya, A. (2015). Multiscale community detection using markov dynamics. (Thesis). University of Hawaii – Manoa. Retrieved from http://hdl.handle.net/10125/100263
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):
Altunkaya, Ali. “Multiscale community detection using markov dynamics.” 2015. Thesis, University of Hawaii – Manoa. Accessed December 14, 2019.
http://hdl.handle.net/10125/100263.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Altunkaya, Ali. “Multiscale community detection using markov dynamics.” 2015. Web. 14 Dec 2019.
Vancouver:
Altunkaya A. Multiscale community detection using markov dynamics. [Internet] [Thesis]. University of Hawaii – Manoa; 2015. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/10125/100263.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Altunkaya A. Multiscale community detection using markov dynamics. [Thesis]. University of Hawaii – Manoa; 2015. Available from: http://hdl.handle.net/10125/100263
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Cornell University
7.
Volz, Erik.
Random Networks with Tunable Degree Distribution and Clustering
.
Degree: 2004, Cornell University
URL: http://hdl.handle.net/1813/226
► We present an algorithm for generating random networks with arbitrary degree distribution and clustering (frequency of triadic closure). We use this algorithm to generate networks…
(more)
▼ We present an algorithm for generating random networks with arbitrary degree distribution and clustering (frequency of triadic closure). We use this algorithm to generate networks with exponential, power law, and poisson degree distributions with variable levels of clustering. Such networks may be used as models of social networks and as a testable null hypothesis about network structure. Finally, we explore the effects of clustering on the point of the phase transition where a giant component forms in a random network, and on the size of the giant component. Some analysis of these effects is presented.
Subjects/Keywords: Complex Networks
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Volz, E. (2004). Random Networks with Tunable Degree Distribution and Clustering
. (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/226
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):
Volz, Erik. “Random Networks with Tunable Degree Distribution and Clustering
.” 2004. Thesis, Cornell University. Accessed December 14, 2019.
http://hdl.handle.net/1813/226.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Volz, Erik. “Random Networks with Tunable Degree Distribution and Clustering
.” 2004. Web. 14 Dec 2019.
Vancouver:
Volz E. Random Networks with Tunable Degree Distribution and Clustering
. [Internet] [Thesis]. Cornell University; 2004. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/1813/226.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Volz E. Random Networks with Tunable Degree Distribution and Clustering
. [Thesis]. Cornell University; 2004. Available from: http://hdl.handle.net/1813/226
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Melbourne
8.
Ahmadizadeh, Saeed.
Synchronisation in signed complex networks motivated by neural population networks.
Degree: 2017, University of Melbourne
URL: http://hdl.handle.net/11343/190564
► Synchronisation is a ubiquitous phenomenon which is observed in a wide range of network applications. The absence or presence of synchronisation depends on network features…
(more)
▼ Synchronisation is a ubiquitous phenomenon which is observed in a wide range of network applications. The absence or presence of synchronisation depends on network features which are characterised by two factors: (i)~dynamics of each subsystem and (ii) network topology. This thesis develops tools for analysing synchronisation via the aforementioned two factors. Our results concerning the network topology are quite general, and, the rest of results are tailored for networks of neural populations. Nevertheless, they can be adopted for other classes of networks. Neural mass populations describe the averaged activity of cortical ensembles which, from the modelling perspective, is particularly important for epilepsy, as this is the scale observed through clinical electroencephalographic (EEG) and intracranial EEG recordings. From the analysis point of view, these networks pose new challenges in studying synchronisation, as their models are non-linear and their interconnections are directed with both positive and negative weights which, in network science, are known as directed signed graph networks.
First, bifurcation analyses are conducted to explore various ranges of behaviours that can be generated by a simple network of neural populations by initialising it around equilibrium points. The underlying network contains two neural populations; each of them is modelled by a well-known Jansen-Rit model. For different values of interconnection gains, bifurcation diagrams are presented, where the input to the neural regions is the bifurcation parameter. The bifurcation analyses reveal that this network can generate various unexpected oscillatory activities, such as delta wave. Surprisingly, by changing the value of the input in one neural population, another neural population shows spike-wave output observed during epilepsy. Using human clinical data, we also investigated the suitability of these models for monitoring seizures.
To analyse a larger network of neural populations, we use mathematical tools from control theory and graph theory to study synchronisation in Wilson-Cowan oscillators. This class of oscillators describes the activity of both excitatory and inhibitory populations of neurons and reproduces self-sustained oscillations observed in EEG signals. Since the interest is to investigate synchronisation with respect to a specific limit-cycle, that is responsible for producing oscillatory output, we linearise the model of the whole network around that limit-cycle, leading to a periodic linear time-varying system. By applying an appropriate change of coordinates, we transform the linearised system to a new system that itself contains a number of subsystems. We then observe that stability~(instability) of those subsystems, excluding one of them, implies the presence~(absence) of synchronous behaviour in the original network. The stability/instability of aforementioned subsystems depends on parameters of the models, as well as eigenvalues of a so-called ``Laplacian matrix" which solely depends on…
Subjects/Keywords: synchronisation; complex networks; signed graphs
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ahmadizadeh, S. (2017). Synchronisation in signed complex networks motivated by neural population networks. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/190564
Chicago Manual of Style (16th Edition):
Ahmadizadeh, Saeed. “Synchronisation in signed complex networks motivated by neural population networks.” 2017. Doctoral Dissertation, University of Melbourne. Accessed December 14, 2019.
http://hdl.handle.net/11343/190564.
MLA Handbook (7th Edition):
Ahmadizadeh, Saeed. “Synchronisation in signed complex networks motivated by neural population networks.” 2017. Web. 14 Dec 2019.
Vancouver:
Ahmadizadeh S. Synchronisation in signed complex networks motivated by neural population networks. [Internet] [Doctoral dissertation]. University of Melbourne; 2017. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/11343/190564.
Council of Science Editors:
Ahmadizadeh S. Synchronisation in signed complex networks motivated by neural population networks. [Doctoral Dissertation]. University of Melbourne; 2017. Available from: http://hdl.handle.net/11343/190564
University of Michigan
9.
Zhang, Xiao.
Statistical Inference and Spectral Methods for Network Analysis.
Degree: PhD, Physics, 2017, University of Michigan
URL: http://hdl.handle.net/2027.42/138753
► There has been increasing interest in the study of networked systems such as biological, technological and social networks in recent years. The purpose of my…
(more)
▼ There has been increasing interest in the study of networked systems such as biological, technological and social
networks in recent years. The purpose of my thesis is to develop and propose new theoretical models and algorithms for analyzing large and
complex networks. I focus on introducing two major classes of techniques – statistical inference and spectral methods and how we can apply those techniques to study various
networks.
Community structures are common in real
networks. Identifying community structures in
networks may shed insights into how the
networks function and offer clear ways for visualizing the
networks. Therefore the problem of community detection is very important in the study of
networks. Spectral methods, algorithms that make use of the eigenvalues and eigenvectors of matrix representations of
networks, are important methods for solving community detection problems. My contributions to spectral methods are two folds: I first present a method to analytically compute the graph spectra of a family of
networks. Using recent advances in computational random matrix theory, I show that spectral algorithms for community detection in
networks are limited by the strength of the embedded signal. My other contribution is extending existing single-step spectral algorithms, which are limited to dividing a network into only two or three communities, to a principled multiway spectral community detection algorithm that divides a network into any number of communities. The algorithm is proven to work better than the widely used k-means heuristic algorithm for multiway spectral community detection. My contributions to statistical inference are coming up with new models with large-scale structures and proposing learning algorithms for fitting real-world
networks into those models. In particular, I give an efficient algorithm for identifying core-periphery structures in
networks. Core-periphery structures are another type of large-scale structures that are commonly found in real-world
networks and they characterize
complex sets of interaction between nodes in networked systems. However, a good algorithm for identifying core-periphery structures in
networks has been missing. I propose an expectation maximization algorithm for dividing a network into a densely connected core and a loosely connected periphery. The algorithm applies belief propagation for computing the optimal decomposition and is therefore efficient and principled. Lastly, I apply statistical inference methods to the study of dynamic
networks. Most real-world
networks are dynamic in nature, that is they often change from time to time. There are increasing but yet little research efforts focus on the study of dynamic
networks. I propose dynamic generalizations of a number of well-known static network models as well as the corresponding statistical learning algorithms for fitting data into those dynamic network models. I then illustrate the use of the algorithm using both computer-generated
networks and real-world examples.
Advisors/Committee Members: Newman, Mark E (committee member), Doering, Charles R (committee member), Deegan, Robert David (committee member), Mao, Xiaoming (committee member), Nadakuditi, Raj Rao (committee member).
Subjects/Keywords: complex networks; Physics; Science
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APA (6th Edition):
Zhang, X. (2017). Statistical Inference and Spectral Methods for Network Analysis. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138753
Chicago Manual of Style (16th Edition):
Zhang, Xiao. “Statistical Inference and Spectral Methods for Network Analysis.” 2017. Doctoral Dissertation, University of Michigan. Accessed December 14, 2019.
http://hdl.handle.net/2027.42/138753.
MLA Handbook (7th Edition):
Zhang, Xiao. “Statistical Inference and Spectral Methods for Network Analysis.” 2017. Web. 14 Dec 2019.
Vancouver:
Zhang X. Statistical Inference and Spectral Methods for Network Analysis. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/2027.42/138753.
Council of Science Editors:
Zhang X. Statistical Inference and Spectral Methods for Network Analysis. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138753
Queen Mary, University of London
10.
Iacovacci, Jacopo.
Motif formation and emergence of mesoscopic structure in complex networks.
Degree: PhD, 2017, Queen Mary, University of London
URL: http://qmro.qmul.ac.uk/xmlui/handle/123456789/25897
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765966
► Network structures can encode information from datasets that have a natural representation in terms of networks, for example datasets describing collaborations or social relations among…
(more)
▼ Network structures can encode information from datasets that have a natural representation in terms of networks, for example datasets describing collaborations or social relations among individuals in science or society, as well as from data that can be mapped into graphs due to their intrinsic correlations, such as time series or images. Developing models and algorithms to characterise the structure of complex networks at the micro and mesoscale is thus of fundamental importance to extract relevant information from and to understand real world complex data and systems. In this thesis we will investigate how modularity, a mesoscopic feature observed almost universally in real world complex networks can emerge, and how this phenomenon is related to the appearance of a particular type of network motif, the triad. We will shed light on the role that motifs play in shaping the mesoscale structure of complex networks by considering two special classes of networks, multiplex networks, that describe complex systems where interactions of different nature are involved, and visibility graphs, a family of graphs that can be extracted from the time series of dynamical processes.
Subjects/Keywords: Mathematical Sciences; Complex networks
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APA (6th Edition):
Iacovacci, J. (2017). Motif formation and emergence of mesoscopic structure in complex networks. (Doctoral Dissertation). Queen Mary, University of London. Retrieved from http://qmro.qmul.ac.uk/xmlui/handle/123456789/25897 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765966
Chicago Manual of Style (16th Edition):
Iacovacci, Jacopo. “Motif formation and emergence of mesoscopic structure in complex networks.” 2017. Doctoral Dissertation, Queen Mary, University of London. Accessed December 14, 2019.
http://qmro.qmul.ac.uk/xmlui/handle/123456789/25897 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765966.
MLA Handbook (7th Edition):
Iacovacci, Jacopo. “Motif formation and emergence of mesoscopic structure in complex networks.” 2017. Web. 14 Dec 2019.
Vancouver:
Iacovacci J. Motif formation and emergence of mesoscopic structure in complex networks. [Internet] [Doctoral dissertation]. Queen Mary, University of London; 2017. [cited 2019 Dec 14].
Available from: http://qmro.qmul.ac.uk/xmlui/handle/123456789/25897 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765966.
Council of Science Editors:
Iacovacci J. Motif formation and emergence of mesoscopic structure in complex networks. [Doctoral Dissertation]. Queen Mary, University of London; 2017. Available from: http://qmro.qmul.ac.uk/xmlui/handle/123456789/25897 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765966
11.
Shao, Shuai.
Robustness and structure of complex networks.
Degree: PhD, Physics, 2015, Boston University
URL: http://hdl.handle.net/2144/14054
► This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack –…
(more)
▼ This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack – localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks.
Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack – localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system.
In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component P∞. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erd ̋os-R ́enyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent λ. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks are much more vulnerable to localized attack compared with random attack.
In the second part, we extend the tree-like generating function method to incorporating clustering structure in complex networks. We study the robustness of a complex network system, especially a network of networks (NON) with clustering structure in each network. We find that the system becomes less robust as we increase the clustering coefficient of each network. For a partially dependent network system, we also find that the influence of the clustering coefficient on network robustness decreases as we decrease the coupling strength, and the critical coupling strength qc, at which the first-order phase transition changes to second-order, increases as we increase the clustering coefficient.
Subjects/Keywords: Physics; Complex networks; Statistical physics
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APA (6th Edition):
Shao, S. (2015). Robustness and structure of complex networks. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/14054
Chicago Manual of Style (16th Edition):
Shao, Shuai. “Robustness and structure of complex networks.” 2015. Doctoral Dissertation, Boston University. Accessed December 14, 2019.
http://hdl.handle.net/2144/14054.
MLA Handbook (7th Edition):
Shao, Shuai. “Robustness and structure of complex networks.” 2015. Web. 14 Dec 2019.
Vancouver:
Shao S. Robustness and structure of complex networks. [Internet] [Doctoral dissertation]. Boston University; 2015. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/2144/14054.
Council of Science Editors:
Shao S. Robustness and structure of complex networks. [Doctoral Dissertation]. Boston University; 2015. Available from: http://hdl.handle.net/2144/14054
Université Catholique de Louvain
12.
Krings, Gautier.
Extraction of information from large networks.
Degree: 2012, Université Catholique de Louvain
URL: http://hdl.handle.net/2078.1/109685
► Every day, millions of customers of mobile phone operators communicate via phone calls, SMS or MMS. These interactions can be represented by a large network,…
(more)
▼ Every day, millions of customers of mobile phone operators communicate via phone calls, SMS or MMS. These interactions can be represented by a large network, where nodes represent customers, and links are drawn between customers that have had a phone call or exchanged messages. This example is one of the numerous applications in which large networks, mathematical models of interactions between millions of items, are used to represent large datasets of networked systems. The study of the structure of such networks provides useful insights on their organization. In this work, we address three different topics in the extraction of information from large networks.
In the first part of this work, we focus on geographical networks, i.e. networks where every node is associated to geographical coordinates. The availability of this information allows studying how geography influences the creation of links. In particular, the intensity of communication between nodes decreases as a power of the geographical distance that separates them.
Second, we address the topic of networks where links change over time, called dynamical networks. In dynamical networks, new nodes enter or leave the network and the strength of their connections rise and wane during the observation period. A phenomenon that is poorly understood so far is the impact that time scales play on the emergence of different structural properties of dynamical networks. In this second part of the thesis, we show how the dynamics of data-driven networks are composed of a complex interaction of processes having each a different characteristic time scale.
Finally, the last part of this work concerns the detection of communities in networks. Communities are groups of nodes that are densely connected to each other. We propose an improvement of actual multi-level community detection methods in which the intermediate results are used at subsequent steps to avoid unnecessary calculations.
(FSA 3) – UCL, 2012
Advisors/Committee Members: UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique, Blondel, Vincent, Van Dooren, Paul, Delvenne, Jean-Charles, Saramäki, Jari, Smoreda, Zbigniew, Barrat, Alain.
Subjects/Keywords: Complex networks; Geographical networks; Dynamical networks; Mobile phone networks; Community detection
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APA (6th Edition):
Krings, G. (2012). Extraction of information from large networks. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/109685
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):
Krings, Gautier. “Extraction of information from large networks.” 2012. Thesis, Université Catholique de Louvain. Accessed December 14, 2019.
http://hdl.handle.net/2078.1/109685.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Krings, Gautier. “Extraction of information from large networks.” 2012. Web. 14 Dec 2019.
Vancouver:
Krings G. Extraction of information from large networks. [Internet] [Thesis]. Université Catholique de Louvain; 2012. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/2078.1/109685.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Krings G. Extraction of information from large networks. [Thesis]. Université Catholique de Louvain; 2012. Available from: http://hdl.handle.net/2078.1/109685
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of North Texas
13.
Hollingshad, Nicholas W.
A Non-equilibrium Approach to Scale Free Networks.
Degree: 2012, University of North Texas
URL: https://digital.library.unt.edu/ark:/67531/metadc149609/
► Many processes and systems in nature and society can be characterized as large numbers of discrete elements that are (usually non-uniformly) interrelated. These networks were…
(more)
▼ Many processes and systems in nature and society can be characterized as large numbers of discrete elements that are (usually non-uniformly) interrelated. These
networks were long thought to be random, but in the late 1990s, Barabási and Albert found that an underlying structure did in fact exist in many natural and technological
networks that are now referred to as scale free. Since then, researchers have gained a much deeper understanding of this particular form of complexity, largely by combining graph theory, statistical physics, and advances in computing technology. This dissertation focuses on out-of-equilibrium dynamic processes as they unfold on these
complex networks. Diffusion in
networks of non-interacting nodes is shown to be temporally
complex, while equilibrium is represented by a stable state with Poissonian fluctuations. Scale free
networks achieve equilibrium very quickly compared to regular
networks, and the most efficient are those with the lowest inverse power law exponent. Temporally
complex diffusion also occurs in
networks with interacting nodes under a cooperative decision-making model. At a critical value of the cooperation parameter, the most efficient scale free network achieves consensus almost as quickly as the equivalent all-to-all network. This finding suggests that the ubiquity of scale free
networks in nature is due to Zipf's principle of least effort. It also suggests that an efficient scale free network structure may be optimal for real
networks that require high connectivity but are hampered by high link costs.
Advisors/Committee Members: Grigolini, Paolo, Gross, Gunter, Krokhin, Arkadii, Mikler, Armin R..
Subjects/Keywords: Complexity; complex networks; scale free networks; network efficiency; networks; network dynamics
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Georgia Tech
14.
Jover, Luis F.
Infection networks, life-history traits, and dynamics in complex virus-microbe systems.
Degree: PhD, Physics, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/55560
► Bacteria and their viral parasites, i.e., phages, are found in natural environments from oceans, soils to the human gut. Phages are key players in ecosystems…
(more)
▼ Bacteria and their viral parasites, i.e., phages, are found in natural environments from oceans, soils to the human gut. Phages are key players in ecosystems responsible for a significant portion of microbial mortality. Individual phages can infect a subset of bacteria types in a community as part of
complex infection
networks. In this thesis we study the interplay between infection
networks, life-history traits, and the resulting dynamics in systems with multiple host and phage types. First, we study the trade-off necessary for the coexistence of multiple hosts and phages in systems with statistically realistic infection
networks. Second, we study how the trends of network architecture vs. biodiversity depend on life-history traits. Finally, we put forward a method for reconstructing infection
networks using measurements of the densities from the dynamics.
Advisors/Committee Members: Weitz, Joshua S (advisor), Weisenfeld, Kurt (committee member), Romber, Justin (committee member), Sponberg, Simon (committee member), Brown, Sam (committee member).
Subjects/Keywords: virus-bacteria; complex networks; population dynamics
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APA (6th Edition):
Jover, L. F. (2016). Infection networks, life-history traits, and dynamics in complex virus-microbe systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55560
Chicago Manual of Style (16th Edition):
Jover, Luis F. “Infection networks, life-history traits, and dynamics in complex virus-microbe systems.” 2016. Doctoral Dissertation, Georgia Tech. Accessed December 14, 2019.
http://hdl.handle.net/1853/55560.
MLA Handbook (7th Edition):
Jover, Luis F. “Infection networks, life-history traits, and dynamics in complex virus-microbe systems.” 2016. Web. 14 Dec 2019.
Vancouver:
Jover LF. Infection networks, life-history traits, and dynamics in complex virus-microbe systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/1853/55560.
Council of Science Editors:
Jover LF. Infection networks, life-history traits, and dynamics in complex virus-microbe systems. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55560
Kent State University
15.
McCallen, Scott J.
Mining Dynamic Structures in Complex Networks.
Degree: MS, College of Arts and Sciences / Department of Computer
Science, 2007, Kent State University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279
► Complex networks have attracted much attention across many scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. Most of the…
(more)
▼ Complex networks have attracted much
attention across many scientific disciplines ranging from sociology
to microbiology to describe systems of interacting units. Most of
the studies have mainly focused on the topology of the network.
However, in many real world applications, the edges and vertices
have associated attributes that are frequently represented as edge
or vertex weights. Furthermore, these weights are often not static,
instead changing with time and forming a time series. Hence, to
fully understand the dynamics of a
complex network, we must
consider both topology and related time series data. Despite the
rapid accumulation of such data, understanding the dynamic nature
of
complex networks remains a complicated and mostly unexplored
task. In this work, we propose two novel mining
approaches to identify dynamic structures that account for both
temporal and topological characteristics in
complex networks. The
first approach is the definition and identification of time series
trends and trend motifs. A trend motif describes a recurring
subgraph where all of its vertices or edges display similar
temporal trends. Given this, each occurrence can help reveal
significant events in a
complex system and frequent motifs may aid
in uncovering dynamic rules of change for the system. In our second
approach, we define the dynamic module, which expands and improves
upon our first model. Essentially, a dynamic module is a set of
connected vertices where the time series associated with each
vertex forms certain structures in the temporal
domain. We have developed efficient mining
algorithms to extract these interesting dynamic structures and our
experimental validation using datasets ranging from the stock
market, world trade, to a protein interaction network, has
demonstrated the efficiency and effectiveness of our
approach.
Advisors/Committee Members: Jin, Ruoming (Advisor).
Subjects/Keywords: Computer Science; complex networks data mining
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APA (6th Edition):
McCallen, S. J. (2007). Mining Dynamic Structures in Complex Networks. (Masters Thesis). Kent State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279
Chicago Manual of Style (16th Edition):
McCallen, Scott J. “Mining Dynamic Structures in Complex Networks.” 2007. Masters Thesis, Kent State University. Accessed December 14, 2019.
http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279.
MLA Handbook (7th Edition):
McCallen, Scott J. “Mining Dynamic Structures in Complex Networks.” 2007. Web. 14 Dec 2019.
Vancouver:
McCallen SJ. Mining Dynamic Structures in Complex Networks. [Internet] [Masters thesis]. Kent State University; 2007. [cited 2019 Dec 14].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279.
Council of Science Editors:
McCallen SJ. Mining Dynamic Structures in Complex Networks. [Masters Thesis]. Kent State University; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=kent1204154279
Penn State University
16.
Gomez Tejeda Zanudo, Jorge.
Network-based dynamic modeling and control strategies in
complex diseases.
Degree: PhD, Physics, 2016, Penn State University
URL: https://etda.libraries.psu.edu/catalog/27687
► In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge…
(more)
▼ In order to understand how the interactions of
molecular components inside cells give rise to cellular function,
creating models that incorporate the current biological knowledge
while also making testable predictions that guide experimental work
is of utmost importance. Creating such models is a challenging task
in complex diseases such as cancer, in which numerous components
are known to play an important role. To model the dynamics of the
networks underlying complex diseases I use network-based models
with discrete dynamics, which have been shown to reproduce the
qualitative dynamics of a multitude of cellular systems while
requiring only the combinatorial nature of the interactions and
qualitative information on the desired/undesired states. I
developed analytical and computational tools based on a type of
function-dependent subnetwork that stabilizes in a steady state
regardless of the state of the rest of the network, and which I
termed stable motif. Based on the concept of stable motif, I
proposed a method to identify a model's dynamical attractors, which
have been found to be identifiable with the cell fates and cell
behaviors of modeled organisms. I also proposed a
stable-motif-based control method that identifies targets whose
manipulation ensures the convergence of the model towards an
attractor of interest. The identified control targets can be single
or multiple nodes, are proven to always drive any initial condition
to the desired attractor, and need to be applied only transiently
to be effective. I illustrated the potential of these methods by
collaborating with wet-lab cancer biologists to construct and
analyze a model for a process involved in the spread of cancer
cells (epithelial-mesenchymal transition), and also applied them to
several published models for complex diseases, such as a type of
white blood cell cancer (T-LGL leukemia). These methods allowed me
to find attractors of larger models than what was previously
possible, identify the subnetworks responsible for the disease and
the healthy cell states, and show that stabilizing the activity of
a few select components can drive the cell towards a desired fate
or away from an undesired fate, the validity of which is supported
by experimental work.
Subjects/Keywords: Systems Biology; Complex Networks; Network models;
Cancer
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APA (6th Edition):
Gomez Tejeda Zanudo, J. (2016). Network-based dynamic modeling and control strategies in
complex diseases. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/27687
Chicago Manual of Style (16th Edition):
Gomez Tejeda Zanudo, Jorge. “Network-based dynamic modeling and control strategies in
complex diseases.” 2016. Doctoral Dissertation, Penn State University. Accessed December 14, 2019.
https://etda.libraries.psu.edu/catalog/27687.
MLA Handbook (7th Edition):
Gomez Tejeda Zanudo, Jorge. “Network-based dynamic modeling and control strategies in
complex diseases.” 2016. Web. 14 Dec 2019.
Vancouver:
Gomez Tejeda Zanudo J. Network-based dynamic modeling and control strategies in
complex diseases. [Internet] [Doctoral dissertation]. Penn State University; 2016. [cited 2019 Dec 14].
Available from: https://etda.libraries.psu.edu/catalog/27687.
Council of Science Editors:
Gomez Tejeda Zanudo J. Network-based dynamic modeling and control strategies in
complex diseases. [Doctoral Dissertation]. Penn State University; 2016. Available from: https://etda.libraries.psu.edu/catalog/27687
Penn State University
17.
Adi, Mohammad.
Using Ants to Find Communities in Complex Networks.
Degree: MS, Computer Science, 2014, Penn State University
URL: https://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.
Subjects/Keywords: ant-algorithms; complex-networks;
community-detection
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APA (6th Edition):
Adi, M. (2014). Using Ants to Find Communities in Complex Networks. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/21544
Chicago Manual of Style (16th Edition):
Adi, Mohammad. “Using Ants to Find Communities in Complex Networks.” 2014. Masters Thesis, Penn State University. Accessed December 14, 2019.
https://etda.libraries.psu.edu/catalog/21544.
MLA Handbook (7th Edition):
Adi, Mohammad. “Using Ants to Find Communities in Complex Networks.” 2014. Web. 14 Dec 2019.
Vancouver:
Adi M. Using Ants to Find Communities in Complex Networks. [Internet] [Masters thesis]. Penn State University; 2014. [cited 2019 Dec 14].
Available from: https://etda.libraries.psu.edu/catalog/21544.
Council of Science Editors:
Adi M. Using Ants to Find Communities in Complex Networks. [Masters Thesis]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/21544
University of Saskatchewan
18.
Guo, Qi 1993-.
Logistic Operator Equation and the Induced Stochastic Process for Complex System Modelling.
Degree: 2018, University of Saskatchewan
URL: http://hdl.handle.net/10388/11224
► The logistic operator equation (LOE) is a type of a multidimensional system of nonlinear ordinary differential equations developed from the classical logistic equation (LE), which…
(more)
▼ The logistic operator equation (LOE) is a type of a multidimensional system of nonlinear ordinary differential equations developed from the classical logistic equation (LE), which has been devised as a theoretical model for dynamically changing
complex networks. According to the choice of its constituent parameters, the LOE can display a number of essentially distinct dynamical characteristics. The connection between a specific LOE and its corresponding
complex network is established by interpreting the dependent variable as an adjacency matrix of the network graph.
Preexisting studies of the LOE were based upon the Dirichlet series playing the role of an a priori Ansatz for the form of solutions. In this thesis we extend those results by replacing the Dirichlet series with the power series as well as the Fourier series. This leads to new types of solutions for the LOE and, consequently, new examples of
complex network dynamics. The solutions are studied via rigorous theoretical calculations as well as via MATLAB and Cytoscape simulations.
In addition, the LOE model admits a natural randomization that transforms a deterministic dynamical solution into a stochastic process. A large part of this work is devoted to the study of stochastic processes of this type. In particular, we have been able to demonstrate that in some special cases the given stochastic model is equivalent to a multi-dimensional stochastic differential equation (SDE). However, the general case is extremely hard to tackle and open questions still abound. To illustrate what is involved we calculate certain expectations related to the general LOE-based stochastic process. This approach may be compared to the study of weak solutions of classical SDE via the Fokker-Plank equation.
The study of the LOE and LOE-based stochastic processes is a new direction in
Complex Network Theory.
Advisors/Committee Members: Sowa, Artur, Spiteri, Raymond, Khan, Shahedul, Wu, Fangxiang.
Subjects/Keywords: Complex networks; Operator equation; Stochastic process; Simulation
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APA (6th Edition):
Guo, Q. 1. (2018). Logistic Operator Equation and the Induced Stochastic Process for Complex System Modelling. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/11224
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):
Guo, Qi 1993-. “Logistic Operator Equation and the Induced Stochastic Process for Complex System Modelling.” 2018. Thesis, University of Saskatchewan. Accessed December 14, 2019.
http://hdl.handle.net/10388/11224.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Guo, Qi 1993-. “Logistic Operator Equation and the Induced Stochastic Process for Complex System Modelling.” 2018. Web. 14 Dec 2019.
Vancouver:
Guo Q1. Logistic Operator Equation and the Induced Stochastic Process for Complex System Modelling. [Internet] [Thesis]. University of Saskatchewan; 2018. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/10388/11224.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Guo Q1. Logistic Operator Equation and the Induced Stochastic Process for Complex System Modelling. [Thesis]. University of Saskatchewan; 2018. Available from: http://hdl.handle.net/10388/11224
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Universitat Politècnica de Catalunya
19.
Rosas Casals, Martí.
Topological Complexity of the Electricity Transmissión Network. Implications in the Sustainability Paradigm.
Degree: 2009, Universitat Politècnica de Catalunya
URL: http://hdl.handle.net/10803/5824
► This Thesis explores the structure, dynamics and evolution of the electricity transmission network from a complex systems perspective, its main objective being the definition of…
(more)
▼ This Thesis explores the structure, dynamics and evolution of the electricity transmission network from a
complex systems perspective, its main objective being the definition of new criteria and tools to help to design a more efficient and sustainable transmission power grid. In doing so, two data sets have been explored and analyzed. On one hand, the Union for the Coordination of Transport of Electricity (UCTE) network, which associates most of the continental Europe national power grid operators in order to coordinate the production and demand of some annual 2300 TWh of energy and 450 million customers from 24 countries. On the other hand, the Gestionaire du Réseau du Transport d'Electricité (RTE) transport network historical evolution, responsible for operating, maintaining and developing the biggest national network in Europe, the French electricity transmission network. The results obtained so far show statistically significant dissimilarities in the structure of the power grids, clearly defining and enclosing particular dynamic behaviors that enable us to segregate European
networks in two sets, namely fragile and robust. Fragile
networks are characterized by meshed topologies and non random structures while robust ones share more randomly generated topologies. The consequences of these finding for the sustainability of infrastructure
networks are significant in terms of cost and risk assessment. A model for the evolution of a power grid network is also presented. We suggest that global topological fragility increases when local connectivity schemes are adapted in order to increase local reliability. These outcomes appeal for new power grid design methods and tools capable to include these new topological aspects into efficiency and reliability assessments
Advisors/Committee Members: Universitat Politècnica de Catalunya. Càtedra Unesco de Sostenibilitat, [email protected] (authoremail), false (authoremailshow), Solé Vicente, Ricard, 1962- (director), Valverde Castillo, Sergi (codirector).
Subjects/Keywords: sustainability; power grid; complex networks; complexity; 504
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APA (6th Edition):
Rosas Casals, M. (2009). Topological Complexity of the Electricity Transmissión Network. Implications in the Sustainability Paradigm. (Thesis). Universitat Politècnica de Catalunya. Retrieved from http://hdl.handle.net/10803/5824
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):
Rosas Casals, Martí. “Topological Complexity of the Electricity Transmissión Network. Implications in the Sustainability Paradigm.” 2009. Thesis, Universitat Politècnica de Catalunya. Accessed December 14, 2019.
http://hdl.handle.net/10803/5824.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Rosas Casals, Martí. “Topological Complexity of the Electricity Transmissión Network. Implications in the Sustainability Paradigm.” 2009. Web. 14 Dec 2019.
Vancouver:
Rosas Casals M. Topological Complexity of the Electricity Transmissión Network. Implications in the Sustainability Paradigm. [Internet] [Thesis]. Universitat Politècnica de Catalunya; 2009. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/10803/5824.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Rosas Casals M. Topological Complexity of the Electricity Transmissión Network. Implications in the Sustainability Paradigm. [Thesis]. Universitat Politècnica de Catalunya; 2009. Available from: http://hdl.handle.net/10803/5824
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Virginia Tech
20.
Khorramzadeh, Yasamin.
Network Reliability: Theory, Estimation, and Applications.
Degree: PhD, Physics, 2015, Virginia Tech
URL: http://hdl.handle.net/10919/64383
► Network reliability is the probabilistic measure that determines whether a network remains functional when its elements fail at random. Definition of functionality varies depending on…
(more)
▼ Network reliability is the probabilistic measure that determines whether a network
remains functional when its elements fail at random. Definition of functionality varies
depending on the problem of interest, thus network reliability has much potential as
a unifying framework to study a broad range of problems arising in
complex network
contexts. However, since its introduction in the 1950's, network reliability has remained
more of an interesting theoretical construct than a practical tool. In large
part, this is due to well-established complexity costs for both its evaluation and approximation,
which has led to the classification of network reliability as a NP-Hard
problem. In this dissertation we present an algorithm to estimate network reliability
and then utilize it to evaluate the reliability of large
networks under various descriptions
of functionality.
The primary goal of this dissertation is to pose network reliability as a general scheme
that provides a practical and efficiently computable observable to distinguish different
networks. Employing this concept, we are able to demonstrate how local structural
changes can impose global consequences. We further use network reliability to assess
the most critical network entities which ensure a network's reliability. We investigate
each of these aspects of reliability by demonstrating some example applications.
Advisors/Committee Members: Eubank, Stephen G. (committeechair), Tauber, Uwe C. (committeechair), Scarola, Vito W. (committee member), Pleimling, Michel Jean (committee member), Heremans, Jean Joseph (committee member).
Subjects/Keywords: Complex Networks; Network Reliability; Network Topology
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APA (6th Edition):
Khorramzadeh, Y. (2015). Network Reliability: Theory, Estimation, and Applications. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64383
Chicago Manual of Style (16th Edition):
Khorramzadeh, Yasamin. “Network Reliability: Theory, Estimation, and Applications.” 2015. Doctoral Dissertation, Virginia Tech. Accessed December 14, 2019.
http://hdl.handle.net/10919/64383.
MLA Handbook (7th Edition):
Khorramzadeh, Yasamin. “Network Reliability: Theory, Estimation, and Applications.” 2015. Web. 14 Dec 2019.
Vancouver:
Khorramzadeh Y. Network Reliability: Theory, Estimation, and Applications. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/10919/64383.
Council of Science Editors:
Khorramzadeh Y. Network Reliability: Theory, Estimation, and Applications. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64383
Virginia Tech
21.
Senthil, Rathna.
IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.
Degree: MS, Computer Science, 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, Bodicherla Aditya (committee member), Raghvendra, Sharath (committee member).
Subjects/Keywords: Overlapping Community Detection; Complex Networks; Local Expansion
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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 December 14, 2019.
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. 14 Dec 2019.
Vancouver:
Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Dec 14].
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
University of Adelaide
22.
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 (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 December 14, 2019.
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. 14 Dec 2019.
Vancouver:
Le BD. Community detection in complex networks. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2019 Dec 14].
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
23.
Hindes, Jason.
Waves And Oscillations In Complex Networks
.
Degree: 2015, Cornell University
URL: http://hdl.handle.net/1813/40950
► Network theory has proven a powerful and general framework for studying the effects of complex interaction topology on the dynamics of many real systems in…
(more)
▼ Network theory has proven a powerful and general framework for studying the effects of
complex interaction topology on the dynamics of many real systems in biology, physics and the social sciences, which show a variety of heterogeneous and multi-scale connectivity patterns. Although much work has been done in this field, many open questions remain about what role network topology plays in influencing the behaviors of
complex systems. This dissertation examines the effects of
complex network structure on the formation of collective oscillations and waves. In particular we study the propagation of epidemic fronts in multi-scale
networks, the interplay between mutual and driven synchronization in heterogeneous oscillator
networks, and the emergence of collective transport waves in driven randomly pinned oscillator
networks. Qualitatively new behavior is found, and new reduction and analysis techniques are developed which allow us to understand the relationship between connectivity structure and the dynamics in these processes. Broadly, this work makes unique contributions to the exploration of fully non-equilibrium pattern formation and nonlinear dynamics in
complex networks.
Advisors/Committee Members: Franck,Carl Peter (committeeMember), Sethna,James Patarasp (committeeMember).
Subjects/Keywords: complex networks;
nonlinear dynamics;
waves and oscillations
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Manager
APA (6th Edition):
Hindes, J. (2015). Waves And Oscillations In Complex Networks
. (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/40950
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):
Hindes, Jason. “Waves And Oscillations In Complex Networks
.” 2015. Thesis, Cornell University. Accessed December 14, 2019.
http://hdl.handle.net/1813/40950.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hindes, Jason. “Waves And Oscillations In Complex Networks
.” 2015. Web. 14 Dec 2019.
Vancouver:
Hindes J. Waves And Oscillations In Complex Networks
. [Internet] [Thesis]. Cornell University; 2015. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/1813/40950.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hindes J. Waves And Oscillations In Complex Networks
. [Thesis]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/40950
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Waterloo
24.
El-Awady, Ahmed.
Probabilistic Failure Analysis of Complex Systems with Case Studies in Nuclear and Hydropower Industries.
Degree: 2019, University of Waterloo
URL: http://hdl.handle.net/10012/14742
► Detailed Monte-Carlo simulation of a complex system is the benchmark method used in probabilistic analysis of engineering systems under multiple uncertain sources of failure modes;…
(more)
▼ Detailed Monte-Carlo simulation of a complex system is the benchmark method used in probabilistic analysis of engineering systems under multiple uncertain sources of failure modes; such simulations typically involve a large amount of CPU time. This makes the probabilistic failure analysis of complex systems, having a large number of components and highly nonlinear interrelationships, computationally intractable and challenging. The objective of this thesis is to synthesize existing methods to analyze multifactorial failure of complex systems which includes predicting the probability of the systems failure and finding its main causes under different situations/scenarios. Bayesian Networks (BNs) have potentials in probabilistically representing complex systems, which is beneficial to predicting the systems failure probability and diagnosing its causes using limited data, logic inference, expert knowledge or simulation of system operations. Compared to other graphical representation techniques such as Event Tree Analysis (ETA) and Fault Tree Analysis (FTA), BNs can deal with complex networks that have multiple initiating events and different types of variables in one graphical representation with the ability to predict the effects, or diagnose the causes leading to a certain effect. This thesis proposes a multifactor failure analysis of complex systems using a number of BN-based approaches. In order to overcome limitations of traditional BNs in dealing with computationally intensive systems simulation and the systems having cyclic interrelationships (or feedbacks) among components, Simulation Supported Bayesian Networks (SSBNs) and Markov Chain Simulation Supported Bayesian Networks (MCSSBNs) are respectively proposed. In the latter, Markov Chains and BNs are integrated to acquire analysis for systems with cyclic behavior when needed. Both SSBNs and MCSSBNs have the distinction of decomposing a complex system to many sub-systems, which makes the system easier to understand and faster to be simulated. The efficiency of these techniques is demonstrated first through their application to a pilot system of two dam reservoirs, where the results of SSBNs and MCSSBNs are compared with those of the entire system operations simulation. Subsequently, two real-world problems including failure analysis of hydropower dams and nuclear waste systems are studied. For such complex networks, a bag of tools that depend on logically inferred data and expert knowledge and judgement are proposed for efficiently predicting failure probabilities in cases where limited operational and historical data are available. Results demonstrate that using the proposed SSBN method for estimating the failure probability of a two dam reservoir system of different connections/topologies results in probability estimates in the range of 3%, which are close to those coming from detailed simulation for the same system. Increasing the number of states per BN variables in the states’ discretization stage makes the SSBN results converge to the simulation results.…
Subjects/Keywords: failure analysis; complex systems; Bayesian networks
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
El-Awady, A. (2019). Probabilistic Failure Analysis of Complex Systems with Case Studies in Nuclear and Hydropower Industries. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14742
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):
El-Awady, Ahmed. “Probabilistic Failure Analysis of Complex Systems with Case Studies in Nuclear and Hydropower Industries.” 2019. Thesis, University of Waterloo. Accessed December 14, 2019.
http://hdl.handle.net/10012/14742.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
El-Awady, Ahmed. “Probabilistic Failure Analysis of Complex Systems with Case Studies in Nuclear and Hydropower Industries.” 2019. Web. 14 Dec 2019.
Vancouver:
El-Awady A. Probabilistic Failure Analysis of Complex Systems with Case Studies in Nuclear and Hydropower Industries. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/10012/14742.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
El-Awady A. Probabilistic Failure Analysis of Complex Systems with Case Studies in Nuclear and Hydropower Industries. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14742
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Waterloo
25.
Zhang, Haotian.
Network Robustness: Diffusing Information Despite Adversaries.
Degree: 2012, University of Waterloo
URL: http://hdl.handle.net/10012/6890
► In this thesis, we consider the problem of diffusing information resiliently in networks that contain misbehaving nodes. Previous strategies to achieve resilient information diffusion typically…
(more)
▼ In this thesis, we consider the problem of diffusing information resiliently in networks that contain misbehaving nodes. Previous strategies to achieve resilient information diffusion typically require the normal nodes to hold some global information, such as the topology of the network and the identities of non-neighboring nodes. However, these assumptions are not suitable for large-scale networks and this necessitates our study of resilient algorithms based on only local information.
We propose a consensus algorithm where, at each time-step, each normal node removes the
extreme values in its neighborhood and updates its value as a weighted average of its own value and the remaining values. We show that traditional topological metrics (such as connectivity of the network) fail to capture such dynamics. Thus, we introduce a topological property termed as network robustness and show that this concept, together with its variants, is the key property to characterize the behavior of a class of resilient algorithms that use purely local information.
We then investigate the robustness properties of complex networks. Specifically, we consider common random graph models for complex networks, including the preferential attachment model, the Erdos-Renyi model, and the geometric random graph model, and compare the metrics of connectivity and robustness in these models. While connectivity and robustness are greatly different in general (i.e., there exist graphs which are highly connected but with poor robustness), we show that the notions of robustness and connectivity are equivalent in the preferential attachment model, cannot be very different in the geometric random graph model, and share the same threshold functions in the Erdos-Renyi model, which gives us more insight about the structure of complex networks. Finally, we provide a construction method for robust graphs.
Subjects/Keywords: Information Diffusion; Resilience; Consensus; Complex Networks
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APA ·
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Manager
APA (6th Edition):
Zhang, H. (2012). Network Robustness: Diffusing Information Despite Adversaries. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6890
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):
Zhang, Haotian. “Network Robustness: Diffusing Information Despite Adversaries.” 2012. Thesis, University of Waterloo. Accessed December 14, 2019.
http://hdl.handle.net/10012/6890.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zhang, Haotian. “Network Robustness: Diffusing Information Despite Adversaries.” 2012. Web. 14 Dec 2019.
Vancouver:
Zhang H. Network Robustness: Diffusing Information Despite Adversaries. [Internet] [Thesis]. University of Waterloo; 2012. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/10012/6890.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zhang H. Network Robustness: Diffusing Information Despite Adversaries. [Thesis]. University of Waterloo; 2012. Available from: http://hdl.handle.net/10012/6890
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Vermont
26.
Cotilla-Sanchez, J. Eduardo.
A Complex Network Approach to Analyzing the Structure and Dynamics of Power Grids.
Degree: MS, Electrical Engineering, 2010, University of Vermont
URL: https://scholarworks.uvm.edu/graddis/57
► Electrical energy generation and distribution systems are good examples of complex systems. They include continuous, discrete, and social dynamics. They are operated by millions of…
(more)
▼ Electrical energy generation and distribution systems are good examples of complex systems. They include continuous, discrete, and social dynamics. They are operated by millions of human and non-human (or electro-mechanical) agents, and they show statistical properties found in other complex systems, such as power-law distributions in failure sizes. A number of recent large blackouts in Europe and North America have emphasized the societal importance of understanding these dynamics. Classical electromagnetic analysis alone frequently does not provide the insight required to characterize and mitigate risks in the electricity infrastructure. The objective of this thesis is to obtain insights into the dynamics of power grids using tools from the science of complex systems. In particular, this thesis will compare the topology, electrical structure, and attack/failure tolerance of power grids with those of theoretical graph structures such as regular, random, small-world, and scale-free networks. Simulation results in this thesis will describe the cost of the disturbances as a function of failure or attack sizes. The cost associated with network perturbations is often measured by changes on the diameter or average path length, whereas in the electricity industry, the loss of power demand (or blackout size) is the best indicator of the cost or impact of disturbances to electricity infrastructure.
Subjects/Keywords: Critical Infrastructures; Power grids; COmplex Networks
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Export
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Manager
APA (6th Edition):
Cotilla-Sanchez, J. E. (2010). A Complex Network Approach to Analyzing the Structure and Dynamics of Power Grids. (Thesis). University of Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/57
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):
Cotilla-Sanchez, J Eduardo. “A Complex Network Approach to Analyzing the Structure and Dynamics of Power Grids.” 2010. Thesis, University of Vermont. Accessed December 14, 2019.
https://scholarworks.uvm.edu/graddis/57.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Cotilla-Sanchez, J Eduardo. “A Complex Network Approach to Analyzing the Structure and Dynamics of Power Grids.” 2010. Web. 14 Dec 2019.
Vancouver:
Cotilla-Sanchez JE. A Complex Network Approach to Analyzing the Structure and Dynamics of Power Grids. [Internet] [Thesis]. University of Vermont; 2010. [cited 2019 Dec 14].
Available from: https://scholarworks.uvm.edu/graddis/57.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Cotilla-Sanchez JE. A Complex Network Approach to Analyzing the Structure and Dynamics of Power Grids. [Thesis]. University of Vermont; 2010. Available from: https://scholarworks.uvm.edu/graddis/57
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Notre Dame
27.
Yuriy Hulovatyy.
Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>.
Degree: PhD, Computer Science and Engineering, 2016, University of Notre Dame
URL: https://curate.nd.edu/show/1r66j100v5f
► Networks provide a natural and powerful way to model complex real-world systems in various domains. Studying structure of a network can help extract functional…
(more)
▼ Networks provide a natural and powerful way
to model
complex real-world systems in various domains. Studying
structure of a network can help extract functional knowledge about
the corresponding system. As real-world
networks exhibit
non-trivial organization at many scales, this extraction can be
done on different levels: from the global perspective of the whole
network to the intermediate perspective of node groups (or
communities) to the local perspective of individual nodes. With new
technological advances, the amount of available real-world network
data in different domains rapidly increases. In addition,
networks
are growing in size and complexity. For example, whereas
traditional network data has been static, because it has become
easier to record system evolution, more of dynamic network data is
becoming available. For these reasons, it is critical to develop
novel computational strategies for efficient extraction of
functional information from the structure of such
complex (e.g.,
dynamic)
networks. And this is the main focus of this dissertation.
We achieve this goal in two different ways, by: 1) answering novel
research questions via established network approaches, and 2)
developing novel network approaches for established research
questions. In the first context, we apply global
network analysis to answer a novel question in a novel domain in
which network research has not been used to date – interpreting
affective physiological data. In addition, we employ local network
analysis to study the interplay between individuals’ social
interactions and traits from a new dynamic (rather than traditional
static) network viewpoint. In the second context,
we take a well-established local analysis approach for static
networks to develop a novel method for the problem of link
prediction, which we use for de-noising biological
networks.
Moreover, we take the same static local approach and develop new
theory for dynamic network analysis. We demonstrate that accounting
for temporal information helps and use our method to study human
aging from biological
networks. Finally, we introduce a new
approach for studying dynamic
networks from the intermediate
perspective, which deals with the problem of segment community
detection. We show that our approach outperforms existing methods
in terms of both accuracy and computational
complexity.
Advisors/Committee Members: Sidney D'Mello, Committee Member, Nitesh Chawla, Committee Member, Aaron Striegel, Committee Member, Tijana Milenkovic, Research Director.
Subjects/Keywords: Complex networks; Network evolution; Network analysis
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APA (6th Edition):
Hulovatyy, Y. (2016). Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/1r66j100v5f
Chicago Manual of Style (16th Edition):
Hulovatyy, Yuriy. “Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>.” 2016. Doctoral Dissertation, University of Notre Dame. Accessed December 14, 2019.
https://curate.nd.edu/show/1r66j100v5f.
MLA Handbook (7th Edition):
Hulovatyy, Yuriy. “Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>.” 2016. Web. 14 Dec 2019.
Vancouver:
Hulovatyy Y. Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2016. [cited 2019 Dec 14].
Available from: https://curate.nd.edu/show/1r66j100v5f.
Council of Science Editors:
Hulovatyy Y. Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>. [Doctoral Dissertation]. University of Notre Dame; 2016. Available from: https://curate.nd.edu/show/1r66j100v5f
University of Notre Dame
28.
Troy Raeder.
Shopping with Networks: An Approach to Market Basket
Analysis</h1>.
Degree: MSin Computer Science and Engineering, Computer Science and Engineering, 2009, University of Notre Dame
URL: https://curate.nd.edu/show/8p58pc30988
► The market basket problem, the search for meaningful associations in customer purchase data, is one of the oldest problems in data mining. The typical…
(more)
▼ The market basket problem, the search for
meaningful associations in customer purchase data, is one of the
oldest problems in data mining. The typical solution involves the
mining and analysis of association rules, which take the form of
statements such as “people who buy diapers are likely to buy beer.‘
It is well-known, however, that typical transaction datasets can
support hundreds or thousands of obvious association rules for each
interesting rule, and filtering through the rules is a non-trivial
task. One may use an interestingness measure to quantify the
usefulness of various rules, but there is no single agreed-upon
measure and different measures can result in very different
rankings of association rules. In this thesis,
we take a different approach to mining transaction data. By
modeling the data as a product network, we discover more expressive
communities (clusters) in the data, which can then be targeted for
further analysis. We demonstrate that the network based approach
can isolate influence among products without excessive ambiguous
associations. We further consider a collaborative marketplace,
where it may be beneficial for the market for stores to share their
product
networks. To that end, we propose a robust privacy
preserving protocol that encourages stores to share their product
network without compromising their individual information. We
demonstrate the effectiveness of the product
networks and the
privacy preserving protocol on a real-world store data. Finally, we
build upon our experience with product
networks to propose a
comprehensive analysis strategy by combining both traditional and
network-based techniques.
Advisors/Committee Members: Nitesh V. Chawla, Committee Chair, Patrick J. Flynn, Committee Member, Marina Blanton, Committee Member.
Subjects/Keywords: Community Detection; Complex Networks; Market Basket Analysis
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APA (6th Edition):
Raeder, T. (2009). Shopping with Networks: An Approach to Market Basket
Analysis</h1>. (Masters Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/8p58pc30988
Chicago Manual of Style (16th Edition):
Raeder, Troy. “Shopping with Networks: An Approach to Market Basket
Analysis</h1>.” 2009. Masters Thesis, University of Notre Dame. Accessed December 14, 2019.
https://curate.nd.edu/show/8p58pc30988.
MLA Handbook (7th Edition):
Raeder, Troy. “Shopping with Networks: An Approach to Market Basket
Analysis</h1>.” 2009. Web. 14 Dec 2019.
Vancouver:
Raeder T. Shopping with Networks: An Approach to Market Basket
Analysis</h1>. [Internet] [Masters thesis]. University of Notre Dame; 2009. [cited 2019 Dec 14].
Available from: https://curate.nd.edu/show/8p58pc30988.
Council of Science Editors:
Raeder T. Shopping with Networks: An Approach to Market Basket
Analysis</h1>. [Masters Thesis]. University of Notre Dame; 2009. Available from: https://curate.nd.edu/show/8p58pc30988
University of Notre Dame
29.
Jason Michael Mayes.
Reduction and Approximation in Large and Infinite
Potential-driven Flow Networks</h1>.
Degree: PhD, Aerospace and Mechanical Engineering, 2012, University of Notre Dame
URL: https://curate.nd.edu/show/ng451g07r7z
► Complex systems often result in intractable mathematical models when classical methodological reduction methods are used in modeling. As a result, reduction methods more holistic…
(more)
▼ Complex systems often result in intractable
mathematical models when classical methodological reduction methods
are used in modeling. As a result, reduction methods more holistic
in nature are normally used to avoid modeling on a component scale.
In this dissertation, several new reduction methods are proposed
with the intent of extending the application of classical
methodological reduction methods to
complex systems. As a
motivating example, a large scale self-similar potential-driven
tree network is used as a model
complex system.
In the linear case, the self-similarity present in the
physical system is translated to a self-similarity in the
mathematical model. This is in turn used to analytically reduce an
otherwise intractable DAE system to a much simpler ODE. It is also
shown that for very large systems, it can sometimes be advantageous
to approximate the system as infinite in scale. In the non-linear
case, two numerical algorithms are presented to simplify dynamic
analysis of piping
networks. These methods are based on Chorin’s
multi-step projection method for solving the Navier-Stokes
equations. In addition to the self-similar tree network, other
self-similar network structures are considered. In particular,
grid-like
networks are considered, and potential reduction methods
are proposed. Finally, in the course of studying
the self-similar potential-driven tree network the appearance of
fractional-order derivatives is noted several times. Based on this
observation, fractional-order system identification is proposed as
an extension of typical black-box reduction methods and
experimental data acquired from a shell-and-tube heat exchanger is
used to demonstrate its usefulness. While the
analysis presented is in terms of potential-driven transport
networks, it should be noted that the specific examples used were
chosen for their simplicity. But the methods used should be more
generally applied to the reduction of self-similar
complex systems,
and even more generally, to large equation sets and DAE systems.
Futhermore, the repeated appearance of fractional-order operators,
and as a special case, fractional-order derivatives and integrals,
suggest a very rich relationship between self-similar
complex
systems and fractional calculus.
Advisors/Committee Members: Dr. Mihir Sen, Committee Member.
Subjects/Keywords: Complex Systems; Self-similar; similarity; networks; trees
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Mayes, J. M. (2012). Reduction and Approximation in Large and Infinite
Potential-driven Flow Networks</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/ng451g07r7z
Chicago Manual of Style (16th Edition):
Mayes, Jason Michael. “Reduction and Approximation in Large and Infinite
Potential-driven Flow Networks</h1>.” 2012. Doctoral Dissertation, University of Notre Dame. Accessed December 14, 2019.
https://curate.nd.edu/show/ng451g07r7z.
MLA Handbook (7th Edition):
Mayes, Jason Michael. “Reduction and Approximation in Large and Infinite
Potential-driven Flow Networks</h1>.” 2012. Web. 14 Dec 2019.
Vancouver:
Mayes JM. Reduction and Approximation in Large and Infinite
Potential-driven Flow Networks</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2012. [cited 2019 Dec 14].
Available from: https://curate.nd.edu/show/ng451g07r7z.
Council of Science Editors:
Mayes JM. Reduction and Approximation in Large and Infinite
Potential-driven Flow Networks</h1>. [Doctoral Dissertation]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/ng451g07r7z
Boston University
30.
Zhou, Di.
Interdependent networks - topological percolation research and application in finance.
Degree: PhD, Physics, 2014, Boston University
URL: http://hdl.handle.net/2144/15118
► This dissertation covers the two major parts of my Ph.D. research: i) developing a theoretical framework of complex networks and applying simulation and numerical methods…
(more)
▼ This dissertation covers the two major parts of my Ph.D. research: i) developing a theoretical framework of complex networks and applying simulation and numerical methods to study the robustness of the network system, and ii) applying statistical physics concepts and methods to quantitatively analyze complex systems and applying the theoretical framework to study real-world systems.
In part I, we focus on developing theories of interdependent networks as well as building computer simulation models, which includes three parts: 1) We report on the effects of topology on failure propagation for a model system consisting of two interdependent networks. We find that the internal node correlations in each of the networks significantly changes the critical density of failures, which can trigger the total disruption of the two-network system. Specifically, we find that the assortativity within a single network decreases the robustness of the entire system. 2) We study the percolation behavior of two interdependent scale-free (SF) networks under random failure of 1-p fraction of nodes. We find that as the coupling strength q between the two networks reduces from 1 (fully coupled) to 0 (no coupling), there exist two critical coupling strengths q1 and q2 , which separate the behaviors of the giant component as a function of p into three different regions, and for q2 < q < q1 , we observe a hybrid order phase transition phenomenon. 3) We study the robustness of n interdependent networks with partially support-dependent relationship both analytically and numerically. We study a starlike network of n Erdos-Renyi (ER), SF networks and a looplike network of n ER networks, and we find for starlike networks, their phase transition regions change with n, but for looplike networks the phase regions change with average degree k .
In part II, we apply concepts and methods developed in statistical physics to study economic systems. We analyze stock market indices and foreign exchange daily returns for 60 countries over the period of 1999-2012. We build a multi-layer network model based on different correlation measures, and introduce a dynamic network model to simulate and analyze the initializing and spreading of financial crisis. Using different computational approaches and econometric tests, we find atypical behavior of the cross correlations and community formations in the financial networks that we study during the financial crisis of 2008. For example, the overall correlation of stock market increases during crisis while the correlation between stock market and foreign exchange market decreases. The dramatic increase in correlations between a specific nation and other nations may indicate that this nation could trigger a global financial crisis. Specifically, core countries that have higher correlations with other countries and larger Gross Domestic Product (GDP) values spread financial crisis quite effectively, yet some countries with small GDPs like Greece and Cyprus are also effective in propagating systemic risk and…
Subjects/Keywords: Physics; Networks; Percolation; Physics; Complex systems
Record Details
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Record Details
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhou, D. (2014). Interdependent networks - topological percolation research and application in finance. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/15118
Chicago Manual of Style (16th Edition):
Zhou, Di. “Interdependent networks - topological percolation research and application in finance.” 2014. Doctoral Dissertation, Boston University. Accessed December 14, 2019.
http://hdl.handle.net/2144/15118.
MLA Handbook (7th Edition):
Zhou, Di. “Interdependent networks - topological percolation research and application in finance.” 2014. Web. 14 Dec 2019.
Vancouver:
Zhou D. Interdependent networks - topological percolation research and application in finance. [Internet] [Doctoral dissertation]. Boston University; 2014. [cited 2019 Dec 14].
Available from: http://hdl.handle.net/2144/15118.
Council of Science Editors:
Zhou D. Interdependent networks - topological percolation research and application in finance. [Doctoral Dissertation]. Boston University; 2014. Available from: http://hdl.handle.net/2144/15118
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Open Access Theses and Dissertations
