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Modular Structure of Complex Networks Open Access


Other title
Complex Networks
Community Detection
Community Evaluation
Network Models
Clustering Agreement
Clustering Networks
Attributed Graphs
Type of item
Degree grantor
University of Alberta
Author or creator
Rabbany khorasgani, Reihaneh
Supervisor and department
Zaïane, Osmar R. (Computing Science)
Examining committee member and department
Schuurmans, Dale (Computing Science)
Salavatipour, Mohammad (Computing Science)
Sander, Jörg (Computing Science)
Ester, Martin (Computing Science)
Department of Computing Science

Date accepted
Graduation date
2016-06:Fall 2016
Doctor of Philosophy
Degree level
Complex networks represent the relationships or interactions between entities in a complex system, such as biological interactions between proteins and genes, hyperlinks between web pages, co-authorships between research scholars. Although drawn from a wide range of domains, real-world networks exhibit similar structural properties and evolution patterns. A fundamental property of these networks is their tendency to organize according to an underlying modular structure, commonly referred to as clustering or community structure. This thesis focuses on comparing, quantifying, modeling, and utilizing this common structure in real-world networks. First, it presents generalizations of well-established traditional clustering criteria and propose proper adaptations to make them applicable in the context of networks. This includes generalizations and extensions of 1) the well-known clustering validity criteria that quantify the goodness of a single clustering; and 2) clustering agreement measures that compare two clusterings of the same dataset. The former introduces a new set of measures for quantifying the goodness of a candid community structure, while the latter establishes a new family of clustering distances suitable for comparing two possible community structures of a given network. These adapted measures are useful in both defining and evaluating the communities in networks. Second, it discusses generative network models and introduces an intuitive and flexible model for synthesizing modular networks that closely comply with the characteristics observed for real-world networks. This network synthesizer is particularly useful for generating benchmark datasets with built-in modular structure, which are used in evaluation of community detection algorithms. Lastly, it investigates how the modular structure of networks can be utilized in different contexts. In particular, it focuses on an e-learning case study, where the network modules can effectively outline the collaboration groups of students, as well as the topics of their discussions; which is used to monitor the participation trends of students throughout an online course. Then, it examines the interplay between the attributes of nodes and their memberships in modules, and present how this interplay can be leveraged for predicting (missing) attribute values; where alternative modular structures are derived, each in better alignment with a given attribute.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
R. Rabbany, O. R. Zaïane, “Generalization of Clustering Agreements and Distances for Overlapping Clusters and Network Communities”, Data Mining and Knowledge Discovery (DAMI), 29(5): 1458-1485, Springer, Jul. 2015.C. Largeron, P-N. Mougel, R. Rabbany, O. R. Zaïane, “Generating Attributed Networks with Communities”, Public Library of Science (PLoS ONE) 10(4), Apr. 2015.R. Rabbany, M. Takaffoli, J. Fagnan, O. R. Zaïane, and R. Campello, “Communities Validity: Methodical Evaluation of Community Mining Algorithms”, Social Network Analysis and Mining (SNAM), 3(4): 1039-1062, Springer, Oct. 2013.R. Rabbany, O. R. Zaïane, “Evaluation of Community Mining Algorithms in the Presence of Attributes”, Trends and Applications in Knowledge Discovery and Data Mining, LNCS 9441: 152-163, Springer, Nov. 2015.R. Rabbany, S. ElAtia, M. Takaffoli, O. R. Zaïane, “Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective”, Educational Data Mining: Applications and Trends, in Studies in Computational Intelligence Series, 524: 441-466, Springer, Nov. 2014.R. Rabbany, M. Takaffoli, J. Fagnan, O. R. Zaïane, and R. Campello, “Relative Validity Criteria for Community Mining Algorithms”, Encyclopedia of Social Network Analysis and Mining, 1562-1576, Springer, Oct. 2014.R. Rabbany, O. R. Zaïane, “Evaluation of Community Mining Algorithms in the Presence of Attributes”, Proceedings of the 4th International Workshop on Quality issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE) at the 19th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), May 2015.R. Rabbany, M. Takaffoli, J. Fagnan, O. R. Zaïane, and R. Campello , “Relative Validity Criteria for Community Mining Algorithms”, Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 258-265, Aug. 2012.R. Rabbany, M. Takaffoli and O. R. Zaïane,“Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques”, Proceedings of the 4th International Conference on Educational Data Mining (EDM), pp. 21-30, Jul. 2011.R. Rabbany, M. Takaffoli and O. R. Zaïane, “Social Network Analysis and Mining to Support the Assessment of Online Student Participation”, ACM SIGKDD Explorations Newsletter, 13 (2): 20-29, 2011.

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