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You searched for subject:(Community detection). Showing records 1 – 30 of 174 total matches.

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1. Ngonmang Kaledje, Christel Blaise. Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux.

Degree: Docteur es, Informatique, 2014, Paris 13

Les réseaux sont présents dans plusieurs contextes et applications : biologie, transports, réseaux sociaux en ligne, etc. De nombreuses applications récentes traitent d'immenses volumes de… (more)

Subjects/Keywords: Détection de communautés; Community detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ngonmang Kaledje, C. B. (2014). Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux. (Doctoral Dissertation). Paris 13. Retrieved from http://www.theses.fr/2014PA132057

Chicago Manual of Style (16th Edition):

Ngonmang Kaledje, Christel Blaise. “Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux.” 2014. Doctoral Dissertation, Paris 13. Accessed March 21, 2019. http://www.theses.fr/2014PA132057.

MLA Handbook (7th Edition):

Ngonmang Kaledje, Christel Blaise. “Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux.” 2014. Web. 21 Mar 2019.

Vancouver:

Ngonmang Kaledje CB. Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux. [Internet] [Doctoral dissertation]. Paris 13; 2014. [cited 2019 Mar 21]. Available from: http://www.theses.fr/2014PA132057.

Council of Science Editors:

Ngonmang Kaledje CB. Detection and dynamic of local communities in large social networks : Détection et dynamique des communautés locales dans les grands réseaux sociaux. [Doctoral Dissertation]. Paris 13; 2014. Available from: http://www.theses.fr/2014PA132057


Cornell University

2. Andrews, June. Community Detection In Large Networks .

Degree: 2012, Cornell University

 Graphs are used to represent various large and complex networks in scientific applications. In order to understand the structure of these graphs, it is useful… (more)

Subjects/Keywords: community detection; social networks

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APA (6th Edition):

Andrews, J. (2012). Community Detection In Large Networks . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/31046

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):

Andrews, June. “Community Detection In Large Networks .” 2012. Thesis, Cornell University. Accessed March 21, 2019. http://hdl.handle.net/1813/31046.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Andrews, June. “Community Detection In Large Networks .” 2012. Web. 21 Mar 2019.

Vancouver:

Andrews J. Community Detection In Large Networks . [Internet] [Thesis]. Cornell University; 2012. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1813/31046.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Andrews J. Community Detection In Large Networks . [Thesis]. Cornell University; 2012. Available from: http://hdl.handle.net/1813/31046

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

3. Zhang, Lingjia. Community detection in network analysis: a survey.

Degree: Statistics, 2016, University of Texas – Austin

 The existence of community structures in networks is not unusual, including in the domains of sociology, biology, and business, etc. The characteristic of the community(more)

Subjects/Keywords: Network analysis; Community detection; Clustering

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APA (6th Edition):

Zhang, L. (2016). Community detection in network analysis: a survey. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/41634

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, Lingjia. “Community detection in network analysis: a survey.” 2016. Thesis, University of Texas – Austin. Accessed March 21, 2019. http://hdl.handle.net/2152/41634.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhang, Lingjia. “Community detection in network analysis: a survey.” 2016. Web. 21 Mar 2019.

Vancouver:

Zhang L. Community detection in network analysis: a survey. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2152/41634.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang L. Community detection in network analysis: a survey. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/41634

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

4. Xu, Jiaming. Statistical inference in networks: fundamental limits and efficient algorithms.

Degree: PhD, 1200, 2015, University of Illinois – Urbana-Champaign

 Today witnesses an explosion of data coming from various types of networks such as online social networks and biological networks. The goal of this thesis… (more)

Subjects/Keywords: Community detection; Networks; Statistical inference

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APA (6th Edition):

Xu, J. (2015). Statistical inference in networks: fundamental limits and efficient algorithms. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72799

Chicago Manual of Style (16th Edition):

Xu, Jiaming. “Statistical inference in networks: fundamental limits and efficient algorithms.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 21, 2019. http://hdl.handle.net/2142/72799.

MLA Handbook (7th Edition):

Xu, Jiaming. “Statistical inference in networks: fundamental limits and efficient algorithms.” 2015. Web. 21 Mar 2019.

Vancouver:

Xu J. Statistical inference in networks: fundamental limits and efficient algorithms. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2142/72799.

Council of Science Editors:

Xu J. Statistical inference in networks: fundamental limits and efficient algorithms. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72799


Penn State University

5. Adi, Mohammad. Using Ants to Find Communities in Complex Networks.

Degree: MS, Computer Science, 2014, Penn State University

 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)

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 March 21, 2019. https://etda.libraries.psu.edu/catalog/21544.

MLA Handbook (7th Edition):

Adi, Mohammad. “Using Ants to Find Communities in Complex Networks.” 2014. Web. 21 Mar 2019.

Vancouver:

Adi M. Using Ants to Find Communities in Complex Networks. [Internet] [Masters thesis]. Penn State University; 2014. [cited 2019 Mar 21]. 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 Toronto

6. Benoit, David Martin. Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling.

Degree: 2017, University of Toronto

Regardless of sampling effort, it is rare to detect all individuals or species in a given survey. This issue, more commonly known as imperfect detection,… (more)

Subjects/Keywords: Community; Detection; Modelling; Occupancy; Stream; 0329

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APA (6th Edition):

Benoit, D. M. (2017). Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/77757

Chicago Manual of Style (16th Edition):

Benoit, David Martin. “Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling.” 2017. Masters Thesis, University of Toronto. Accessed March 21, 2019. http://hdl.handle.net/1807/77757.

MLA Handbook (7th Edition):

Benoit, David Martin. “Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling.” 2017. Web. 21 Mar 2019.

Vancouver:

Benoit DM. Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling. [Internet] [Masters thesis]. University of Toronto; 2017. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1807/77757.

Council of Science Editors:

Benoit DM. Assessing the Impacts of Imperfect Detection in Stream Fish Communities through Multispecies Occupancy Modelling. [Masters Thesis]. University of Toronto; 2017. Available from: http://hdl.handle.net/1807/77757


University of Sydney

7. Amiri, Babak. Evolutionary Algorithms for Community Detection in Complex Networks .

Degree: 2013, University of Sydney

 In recent years there has been a surge of community detection study of complex network analysis, since communities often play important roles in network systems.… (more)

Subjects/Keywords: Community Detection; Evolutionary Algorithms; Modularity; Social Networks

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APA (6th Edition):

Amiri, B. (2013). Evolutionary Algorithms for Community Detection in Complex Networks . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/10451

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Amiri, Babak. “Evolutionary Algorithms for Community Detection in Complex Networks .” 2013. Thesis, University of Sydney. Accessed March 21, 2019. http://hdl.handle.net/2123/10451.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Amiri, Babak. “Evolutionary Algorithms for Community Detection in Complex Networks .” 2013. Web. 21 Mar 2019.

Vancouver:

Amiri B. Evolutionary Algorithms for Community Detection in Complex Networks . [Internet] [Thesis]. University of Sydney; 2013. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2123/10451.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Amiri B. Evolutionary Algorithms for Community Detection in Complex Networks . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/10451

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Cornell University

8. Kloumann, Isabel. Behaviors, Interactions, And Communities In Networks .

Degree: 2016, Cornell University

 Exciting and unexpected patterns can emerge when systems are highly connected, even when they are composed of the simplest objects. In this thesis we investigate… (more)

Subjects/Keywords: Community detection; Machine learning; Data mining

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APA (6th Edition):

Kloumann, I. (2016). Behaviors, Interactions, And Communities In Networks . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/44315

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):

Kloumann, Isabel. “Behaviors, Interactions, And Communities In Networks .” 2016. Thesis, Cornell University. Accessed March 21, 2019. http://hdl.handle.net/1813/44315.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kloumann, Isabel. “Behaviors, Interactions, And Communities In Networks .” 2016. Web. 21 Mar 2019.

Vancouver:

Kloumann I. Behaviors, Interactions, And Communities In Networks . [Internet] [Thesis]. Cornell University; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1813/44315.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kloumann I. Behaviors, Interactions, And Communities In Networks . [Thesis]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44315

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

9. Huang, H. Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm:.

Degree: 2015, Delft University of Technology

 Complex networks are a special type of graph that frequently appears in nature and in many different fields of science and engineering. Studying complex networks… (more)

Subjects/Keywords: complex network; community detection; distributed computing

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APA (6th Edition):

Huang, H. (2015). Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d

Chicago Manual of Style (16th Edition):

Huang, H. “Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm:.” 2015. Masters Thesis, Delft University of Technology. Accessed March 21, 2019. http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d.

MLA Handbook (7th Edition):

Huang, H. “Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm:.” 2015. Web. 21 Mar 2019.

Vancouver:

Huang H. Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2019 Mar 21]. Available from: http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d.

Council of Science Editors:

Huang H. Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d


University of Illinois – Urbana-Champaign

10. Sankagiri, Suryanarayana. Community detection in preferential attachment graphs.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

 This thesis examines the problem of community detection in a new random graph model, which is a generalization of preferential attachment graphs. This model has… (more)

Subjects/Keywords: community detection; preferential attachment graphs; message passing

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APA (6th Edition):

Sankagiri, S. (2018). Community detection in preferential attachment graphs. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102474

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):

Sankagiri, Suryanarayana. “Community detection in preferential attachment graphs.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed March 21, 2019. http://hdl.handle.net/2142/102474.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sankagiri, Suryanarayana. “Community detection in preferential attachment graphs.” 2018. Web. 21 Mar 2019.

Vancouver:

Sankagiri S. Community detection in preferential attachment graphs. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2142/102474.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sankagiri S. Community detection in preferential attachment graphs. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102474

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Houston

11. Chen, Tianlong 1988-. Understanding the Structure of Complex Networks with Community Detection.

Degree: Physics, Department of, University of Houston

 Modularity is the most widely used metric in the field of community detection for complex networks. This dissertation is about the successful integration of modularity… (more)

Subjects/Keywords: Community detection; Modularity

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APA (6th Edition):

Chen, T. 1. (n.d.). Understanding the Structure of Complex Networks with Community Detection. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/2923

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chen, Tianlong 1988-. “Understanding the Structure of Complex Networks with Community Detection.” Thesis, University of Houston. Accessed March 21, 2019. http://hdl.handle.net/10657/2923.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chen, Tianlong 1988-. “Understanding the Structure of Complex Networks with Community Detection.” Web. 21 Mar 2019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Chen T1. Understanding the Structure of Complex Networks with Community Detection. [Internet] [Thesis]. University of Houston; [cited 2019 Mar 21]. Available from: http://hdl.handle.net/10657/2923.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Chen T1. Understanding the Structure of Complex Networks with Community Detection. [Thesis]. University of Houston; Available from: http://hdl.handle.net/10657/2923

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.


University of Notre Dame

12. 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

  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)

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 March 21, 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. 21 Mar 2019.

Vancouver:

Raeder T. Shopping with Networks: An Approach to Market Basket Analysis</h1>. [Internet] [Masters thesis]. University of Notre Dame; 2009. [cited 2019 Mar 21]. 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


Virginia Tech

13. Senthil, Rathna. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.

Degree: MS, Computer Science, 2016, Virginia Tech

 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)

Subjects/Keywords: Overlapping Community Detection; Complex Networks; Local Expansion

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Senthil, R. (2016). IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/65160

Chicago Manual of Style (16th Edition):

Senthil, Rathna. “IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks.” 2016. Masters Thesis, Virginia Tech. Accessed March 21, 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. 21 Mar 2019.

Vancouver:

Senthil R. IDLE: A Novel Approach to Improving Overlapping Community Detection in Complex Networks. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Mar 21]. 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

14. Le, Ba Dung. Community detection in complex networks.

Degree: 2018, University of Adelaide

 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)

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 March 21, 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. 21 Mar 2019.

Vancouver:

Le BD. Community detection in complex networks. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2019 Mar 21]. 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


Australian National University

15. Sun, Xufei. Efficient Community Detection .

Degree: 2015, Australian National University

 Given a large network, local community detection aims at finding the community that contains a set of query nodes and also maximises (minimises) a goodness… (more)

Subjects/Keywords: community detection; large graph; community; local detection; metric; goodness metric; overlapping graphs

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APA (6th Edition):

Sun, X. (2015). Efficient Community Detection . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/16471

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sun, Xufei. “Efficient Community Detection .” 2015. Thesis, Australian National University. Accessed March 21, 2019. http://hdl.handle.net/1885/16471.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sun, Xufei. “Efficient Community Detection .” 2015. Web. 21 Mar 2019.

Vancouver:

Sun X. Efficient Community Detection . [Internet] [Thesis]. Australian National University; 2015. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1885/16471.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sun X. Efficient Community Detection . [Thesis]. Australian National University; 2015. Available from: http://hdl.handle.net/1885/16471

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Utah

16. Yadav, Nitin. Community-affinity: measuring strength of memberships of nodes in network communities.

Degree: MSin Computing, School of Computing, 2015, University of Utah

 Detecting community structure in networks has been a widely studied area. While mostof the methods produce an exclusive membership of the nodes, the nodes in… (more)

Subjects/Keywords: community-affinity; community-detection; graph clustering; graphs; network science

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APA (6th Edition):

Yadav, N. (2015). Community-affinity: measuring strength of memberships of nodes in network communities. (Masters Thesis). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497

Chicago Manual of Style (16th Edition):

Yadav, Nitin. “Community-affinity: measuring strength of memberships of nodes in network communities.” 2015. Masters Thesis, University of Utah. Accessed March 21, 2019. http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497.

MLA Handbook (7th Edition):

Yadav, Nitin. “Community-affinity: measuring strength of memberships of nodes in network communities.” 2015. Web. 21 Mar 2019.

Vancouver:

Yadav N. Community-affinity: measuring strength of memberships of nodes in network communities. [Internet] [Masters thesis]. University of Utah; 2015. [cited 2019 Mar 21]. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497.

Council of Science Editors:

Yadav N. Community-affinity: measuring strength of memberships of nodes in network communities. [Masters Thesis]. University of Utah; 2015. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/4069/rec/497


Purdue University

17. Shanbhaq, Sunanda Vivek. A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems.

Degree: MS, Computer and Information Technology, 2016, Purdue University

  Cyber networks are complex networks with various hosts forming the entities of the network and the communication between them forming the edges of the… (more)

Subjects/Keywords: Applied sciences; Community; Community detection; Modularity; Computer Engineering; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shanbhaq, S. V. (2016). A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems. (Thesis). Purdue University. Retrieved from http://docs.lib.purdue.edu/open_access_theses/814

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shanbhaq, Sunanda Vivek. “A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems.” 2016. Thesis, Purdue University. Accessed March 21, 2019. http://docs.lib.purdue.edu/open_access_theses/814.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shanbhaq, Sunanda Vivek. “A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems.” 2016. Web. 21 Mar 2019.

Vancouver:

Shanbhaq SV. A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems. [Internet] [Thesis]. Purdue University; 2016. [cited 2019 Mar 21]. Available from: http://docs.lib.purdue.edu/open_access_theses/814.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shanbhaq SV. A faster version of Louvain method for community detection for efficient modeling and analytics of cyber systems. [Thesis]. Purdue University; 2016. Available from: http://docs.lib.purdue.edu/open_access_theses/814

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Waterloo

18. Khaniyev, Taghi. Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity.

Degree: 2018, University of Waterloo

 Employing data-driven methods to efficiently solve practical and large optimization problems is a recent trend that focuses on identifying patterns and structures in the problem… (more)

Subjects/Keywords: structure detection; bordered block diagonal; Dantzig-Wolfe decomposition; community detection; hub location; brain connectivity networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Khaniyev, T. (2018). Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13476

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):

Khaniyev, Taghi. “Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity.” 2018. Thesis, University of Waterloo. Accessed March 21, 2019. http://hdl.handle.net/10012/13476.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Khaniyev, Taghi. “Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity.” 2018. Web. 21 Mar 2019.

Vancouver:

Khaniyev T. Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/10012/13476.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khaniyev T. Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13476

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Carnegie Mellon University

19. Benigni, Matthew Curran. Detection and Analysis of Online Extremist Communities.

Degree: 2017, Carnegie Mellon University

 Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be… (more)

Subjects/Keywords: Covert Network Detection; Community Detection; Annotated Networks; Multilayer Networks; Heterogeneous Networks; Spectral Clustering

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APA (6th Edition):

Benigni, M. C. (2017). Detection and Analysis of Online Extremist Communities. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/949

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Benigni, Matthew Curran. “Detection and Analysis of Online Extremist Communities.” 2017. Thesis, Carnegie Mellon University. Accessed March 21, 2019. http://repository.cmu.edu/dissertations/949.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Benigni, Matthew Curran. “Detection and Analysis of Online Extremist Communities.” 2017. Web. 21 Mar 2019.

Vancouver:

Benigni MC. Detection and Analysis of Online Extremist Communities. [Internet] [Thesis]. Carnegie Mellon University; 2017. [cited 2019 Mar 21]. Available from: http://repository.cmu.edu/dissertations/949.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Benigni MC. Detection and Analysis of Online Extremist Communities. [Thesis]. Carnegie Mellon University; 2017. Available from: http://repository.cmu.edu/dissertations/949

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

20. Gupta, Manish. Outlier detection for information networks.

Degree: PhD, 0112, 2013, University of Illinois – Urbana-Champaign

 The study of networks has emerged in diverse disciplines as a means of analyzing complex relationship data. There has been a significant amount of work… (more)

Subjects/Keywords: outlier detection; Community Distribution Outliers (CDOutliers); Evolutionary Community Outliers (ECOutliers); toread; data mining; outlier detection for graphs; outlier detection for networks; graph query processing; community detection; community outliers; anomalies; anomaly detection; evolution in networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gupta, M. (2013). Outlier detection for information networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44770

Chicago Manual of Style (16th Edition):

Gupta, Manish. “Outlier detection for information networks.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 21, 2019. http://hdl.handle.net/2142/44770.

MLA Handbook (7th Edition):

Gupta, Manish. “Outlier detection for information networks.” 2013. Web. 21 Mar 2019.

Vancouver:

Gupta M. Outlier detection for information networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2142/44770.

Council of Science Editors:

Gupta M. Outlier detection for information networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44770

21. Rabbany khorasgani, Reihaneh. Modular Structure of Complex Networks.

Degree: PhD, Department of Computing Science, 2016, University of Alberta

 Complex networks represent the relationships or interactions between entities in a complex system, such as biological interactions between proteins and genes, hyperlinks between web pages,… (more)

Subjects/Keywords: Complex Networks; Community Detection; Community Evaluation; Network Models; Clustering Agreement; Clustering Networks; Attributed Graphs

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rabbany khorasgani, R. (2016). Modular Structure of Complex Networks. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cxk81jk64k

Chicago Manual of Style (16th Edition):

Rabbany khorasgani, Reihaneh. “Modular Structure of Complex Networks.” 2016. Doctoral Dissertation, University of Alberta. Accessed March 21, 2019. https://era.library.ualberta.ca/files/cxk81jk64k.

MLA Handbook (7th Edition):

Rabbany khorasgani, Reihaneh. “Modular Structure of Complex Networks.” 2016. Web. 21 Mar 2019.

Vancouver:

Rabbany khorasgani R. Modular Structure of Complex Networks. [Internet] [Doctoral dissertation]. University of Alberta; 2016. [cited 2019 Mar 21]. Available from: https://era.library.ualberta.ca/files/cxk81jk64k.

Council of Science Editors:

Rabbany khorasgani R. Modular Structure of Complex Networks. [Doctoral Dissertation]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cxk81jk64k


Purdue University

22. Bang, Seokhun. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.

Degree: MSIE, Industrial Engineering, 2015, Purdue University

 Graph clustering is a field of study that helps reveal characteristics of communities. Systems can be viewed as networks and form communities in various areas… (more)

Subjects/Keywords: Clustering algorithm; Community detection; Community structure; Label Propagation Method; LabelRank; Markov Cluster Algorithm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bang, S. (2015). Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. (Thesis). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_theses/1046

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Thesis, Purdue University. Accessed March 21, 2019. https://docs.lib.purdue.edu/open_access_theses/1046.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Web. 21 Mar 2019.

Vancouver:

Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Internet] [Thesis]. Purdue University; 2015. [cited 2019 Mar 21]. Available from: https://docs.lib.purdue.edu/open_access_theses/1046.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Thesis]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_theses/1046

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Purdue University

23. Bang, Seokhun. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.

Degree: MSIE, Industrial Engineering, 2015, Purdue University

  Graph clustering is a field of study that helps reveal characteristics of communities. Systems can be viewed as networks and form communities in various… (more)

Subjects/Keywords: Clustering algorithm; Community detection; Community structure; Label Propagation Method; LabelRank; Markov Cluster Algorithm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bang, S. (2015). Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. (Thesis). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_theses/1037

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Thesis, Purdue University. Accessed March 21, 2019. https://docs.lib.purdue.edu/open_access_theses/1037.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Web. 21 Mar 2019.

Vancouver:

Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Internet] [Thesis]. Purdue University; 2015. [cited 2019 Mar 21]. Available from: https://docs.lib.purdue.edu/open_access_theses/1037.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Thesis]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_theses/1037

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Western Australia

24. Lim, Kwan Hui. The identification of like-minded communities on online social networks.

Degree: MS, 2013, University of Western Australia

The efficient identification of communities with common interests is a key consideration in applying targeted advertising and viral marketing to online social networking sites. Existing… (more)

Subjects/Keywords: Social network analysis; Community detection; Clustering algorithm; Twitter; YouTube

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lim, K. H. (2013). The identification of like-minded communities on online social networks. (Masters Thesis). University of Western Australia. Retrieved from http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=40096&local_base=GEN01-INS01

Chicago Manual of Style (16th Edition):

Lim, Kwan Hui. “The identification of like-minded communities on online social networks.” 2013. Masters Thesis, University of Western Australia. Accessed March 21, 2019. http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=40096&local_base=GEN01-INS01.

MLA Handbook (7th Edition):

Lim, Kwan Hui. “The identification of like-minded communities on online social networks.” 2013. Web. 21 Mar 2019.

Vancouver:

Lim KH. The identification of like-minded communities on online social networks. [Internet] [Masters thesis]. University of Western Australia; 2013. [cited 2019 Mar 21]. Available from: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=40096&local_base=GEN01-INS01.

Council of Science Editors:

Lim KH. The identification of like-minded communities on online social networks. [Masters Thesis]. University of Western Australia; 2013. Available from: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=40096&local_base=GEN01-INS01


Georgia Tech

25. Liang, Yingyu. Modern aspects of unsupervised learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

 Unsupervised learning has become more and more important due to the recent explosion of data. Clustering, a key topic in unsupervised learning, is a well-studied… (more)

Subjects/Keywords: Unsupervised learning; Clustering; Perturbation resilience; Distributed clustering; Community detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liang, Y. (2014). Modern aspects of unsupervised learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52282

Chicago Manual of Style (16th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed March 21, 2019. http://hdl.handle.net/1853/52282.

MLA Handbook (7th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Web. 21 Mar 2019.

Vancouver:

Liang Y. Modern aspects of unsupervised learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1853/52282.

Council of Science Editors:

Liang Y. Modern aspects of unsupervised learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52282


Michigan Technological University

26. Su, Jianhai. MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS.

Degree: MS, Department of Computer Science, 2014, Michigan Technological University

  Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of… (more)

Subjects/Keywords: Fuzzy Community Detection; Fuzzy Modularity Maximization; Genetic Algorithm; Social Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Su, J. (2014). MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS. (Masters Thesis). Michigan Technological University. Retrieved from http://digitalcommons.mtu.edu/etd-restricted/236

Chicago Manual of Style (16th Edition):

Su, Jianhai. “MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS.” 2014. Masters Thesis, Michigan Technological University. Accessed March 21, 2019. http://digitalcommons.mtu.edu/etd-restricted/236.

MLA Handbook (7th Edition):

Su, Jianhai. “MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS.” 2014. Web. 21 Mar 2019.

Vancouver:

Su J. MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS. [Internet] [Masters thesis]. Michigan Technological University; 2014. [cited 2019 Mar 21]. Available from: http://digitalcommons.mtu.edu/etd-restricted/236.

Council of Science Editors:

Su J. MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS. [Masters Thesis]. Michigan Technological University; 2014. Available from: http://digitalcommons.mtu.edu/etd-restricted/236


Université Catholique de Louvain

27. Jungers, Baptiste. Communities in Networks : an empirical and comparative study of some detection methods.

Degree: 2016, Université Catholique de Louvain

Graphs or networks are mathematical structures that consist of elements that can be pairwise linked if some sort of interaction exists between them. Therefore, they… (more)

Subjects/Keywords: Clustering; Community Detection; Condorcet Criterion; Louvain Method; Networks

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APA (6th Edition):

Jungers, B. (2016). Communities in Networks : an empirical and comparative study of some detection methods. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/thesis:7024

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):

Jungers, Baptiste. “Communities in Networks : an empirical and comparative study of some detection methods.” 2016. Thesis, Université Catholique de Louvain. Accessed March 21, 2019. http://hdl.handle.net/2078.1/thesis:7024.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jungers, Baptiste. “Communities in Networks : an empirical and comparative study of some detection methods.” 2016. Web. 21 Mar 2019.

Vancouver:

Jungers B. Communities in Networks : an empirical and comparative study of some detection methods. [Internet] [Thesis]. Université Catholique de Louvain; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/2078.1/thesis:7024.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jungers B. Communities in Networks : an empirical and comparative study of some detection methods. [Thesis]. Université Catholique de Louvain; 2016. Available from: http://hdl.handle.net/2078.1/thesis:7024

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Penn State University

28. Raghavan, Ushanandini. CLUSTERING AND CONNECTIVITY PROBLEMS IN COMPLEX NETWORKS.

Degree: PhD, Industrial Engineering, 2008, Penn State University

 Networked systems pervade this world. From social relationships between people to chemical interactions between bio-molecules to wired or wireless communications between technological devices, networks play… (more)

Subjects/Keywords: networks; social networks; community detection; wireless sensor networks; clustering; connectivity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Raghavan, U. (2008). CLUSTERING AND CONNECTIVITY PROBLEMS IN COMPLEX NETWORKS. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/8598

Chicago Manual of Style (16th Edition):

Raghavan, Ushanandini. “CLUSTERING AND CONNECTIVITY PROBLEMS IN COMPLEX NETWORKS.” 2008. Doctoral Dissertation, Penn State University. Accessed March 21, 2019. https://etda.libraries.psu.edu/catalog/8598.

MLA Handbook (7th Edition):

Raghavan, Ushanandini. “CLUSTERING AND CONNECTIVITY PROBLEMS IN COMPLEX NETWORKS.” 2008. Web. 21 Mar 2019.

Vancouver:

Raghavan U. CLUSTERING AND CONNECTIVITY PROBLEMS IN COMPLEX NETWORKS. [Internet] [Doctoral dissertation]. Penn State University; 2008. [cited 2019 Mar 21]. Available from: https://etda.libraries.psu.edu/catalog/8598.

Council of Science Editors:

Raghavan U. CLUSTERING AND CONNECTIVITY PROBLEMS IN COMPLEX NETWORKS. [Doctoral Dissertation]. Penn State University; 2008. Available from: https://etda.libraries.psu.edu/catalog/8598


UCLA

29. Razaee, Zahra. Community Detection in Networks with Node Covariates.

Degree: Statistics, 2017, UCLA

Community detection or clustering is a fundamental task in the analysis of network data. Most networks come with annotations which can be in form of… (more)

Subjects/Keywords: Statistics; Bipartite; Community Detection; Network; Node Covariates; Stochastic Blockmodel

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Razaee, Z. (2017). Community Detection in Networks with Node Covariates. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/3343v33s

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Razaee, Zahra. “Community Detection in Networks with Node Covariates.” 2017. Thesis, UCLA. Accessed March 21, 2019. http://www.escholarship.org/uc/item/3343v33s.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Razaee, Zahra. “Community Detection in Networks with Node Covariates.” 2017. Web. 21 Mar 2019.

Vancouver:

Razaee Z. Community Detection in Networks with Node Covariates. [Internet] [Thesis]. UCLA; 2017. [cited 2019 Mar 21]. Available from: http://www.escholarship.org/uc/item/3343v33s.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Razaee Z. Community Detection in Networks with Node Covariates. [Thesis]. UCLA; 2017. Available from: http://www.escholarship.org/uc/item/3343v33s

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


UCLA

30. Yu, Xiaolu. Social Network Analysis: Statistical Model, Community Detection and Friend Recommendation.

Degree: Statistics, 2017, UCLA

 In recent years, Social Network Service (SNS) is a novel, popular way to make friends andconvey information online. Therefore, the analysis of network data has… (more)

Subjects/Keywords: Statistics; community detection; computer science; recommendation system; Social network; statistical model

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yu, X. (2017). Social Network Analysis: Statistical Model, Community Detection and Friend Recommendation. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/43w1f0mf

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):

Yu, Xiaolu. “Social Network Analysis: Statistical Model, Community Detection and Friend Recommendation.” 2017. Thesis, UCLA. Accessed March 21, 2019. http://www.escholarship.org/uc/item/43w1f0mf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yu, Xiaolu. “Social Network Analysis: Statistical Model, Community Detection and Friend Recommendation.” 2017. Web. 21 Mar 2019.

Vancouver:

Yu X. Social Network Analysis: Statistical Model, Community Detection and Friend Recommendation. [Internet] [Thesis]. UCLA; 2017. [cited 2019 Mar 21]. Available from: http://www.escholarship.org/uc/item/43w1f0mf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu X. Social Network Analysis: Statistical Model, Community Detection and Friend Recommendation. [Thesis]. UCLA; 2017. Available from: http://www.escholarship.org/uc/item/43w1f0mf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

[1] [2] [3] [4] [5] [6]

.