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Community Identification, Evolution and Prediction in Dynamic Social Networks Open Access

Descriptions

Other title
Subject/Keyword
Community Mining
Social Nerwork
Evolution of Networks
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Takaffoli, Mansoureh
Supervisor and department
Zaiane, Osmar R
Examining committee member and department
Goebel, Randy (Computing Science)
Shiri, Ali (School of Library and Information Studies)
Sander, Joerg (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2015-09-29T16:52:55Z
Graduation date
2015-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Information networks that describe the relationship between individuals are called social networks and are usually modeled by a graph structure. Social network analysis is the study of these information networks which leads to uncovering patterns of interaction among the entities. Community mining provides a higher level of structure and offers greater understanding, but networks change over time. Their constituent communities change, and the elements of those communities change over time as well, i.e. they have fluctuating members and can grow and shrink over time. Examining how the structure of these networks and their communities changes over time provides insights into their evolution patterns, factors that trigger the changes, and ultimately predict the future structure of these networks. Furthermore, this prediction has many important applications, such as recommendation systems and customer targeting. In this PhD research dissertation, we provide a brief overview of the existing research in the area of dynamic social network analysis, and their limitations. Then, we present a framework, called MODEC, for modelling, detecting, and predicting the evolution of communities and individuals over time in a dynamic scenario. We introduce a new incremental community mining approach, in which communities in the current time are obtained based on the communities from the past time frame. Then, with the definition of the critical events and transitions, and applying our event analysis, the evolutions of communities are abstracted in order to see structure in the dynamic change over time. This higher level of analysis has a counterpart that deals with the fine grain changes in community members with relation to their communities or the global network. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. We also define the concept of meta community which is a series of similar communities captured in different timeframes and detected by our matching algorithm. Furthermore, the events detected by the framework are supplemented by the extraction and investigation of the topics discovered for each community, and extensive experimental studies on real datasets, demonstrate the applicability, effectiveness, and soundness of our proposed framework. After analyzing the dynamic of social network, we predict the occurrence of different events and transition for communities. Our framework incorporates key features related to a community -- its structure, history, and influential members, and automatically detects the most predictive features for each event and transition. Our experiments on real world datasets confirm that the evolution of communities can be predicted with a very high accuracy, while we further observe that the most significant features vary for the predictability of each event and transition.
Language
English
DOI
doi:10.7939/R3J960J3T
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Afra Abnar, Mansoureh Takaffoli, Reihaneh Rabbany, and Osmar R. Zaiane. Ssrm: Structural social role mining for dynamic social networks. In International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’14, 2014.Afra Abnar, Mansoureh Takaffoli, Reihaneh Rabbany, and Osmar R. Zaiane. Ssrm: structural social role mining for dynamic social networks. Social Network Analysis and Mining, 5(1), 2015.Jiyang Chen, Justin Fagnan, Randy Goebel, Reihaneh Rabbany, Farzad Sangi, Mansoureh Takaffoli, Eric Verbeek, and Osmar R. Zaiane. Meerkat: Community mining with dynamic social networks. In Proceedings of 10th IEEE International Conference on Data Mining, ICDM ’10, 2010.Justin Fagnan, Reihaneh Rabbany, Mansoureh Takaffoli, Eric Verbeek, and Osmar R. Zaiane. Community dynamics: Event and role analysis in social network analysis. In Proceedings of 10th International Conference on Advanced Data Mining and Applications, ADMA ’14, 2014.Mansoureh Takaffoli. Community evolution in dynamic social networks - challenges and problems. In Proceedings of the IEEE ICDM PhD Student Forum, 2011.Mansoureh Takaffoli, Reihaneh Rabbany, and Osmar R. Zaiane. Incremental local community identification in dynamic social networks. In International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’13, 2013.Mansoureh Takaffoli, Reihaneh Rabbany, and Osmar R. Zaiane. Community evolution prediction in dynamic social networks. In International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’14, 2014.Mansoureh Takaffoli, Farzad Sangi, Justin Fagnan, and Osmar R. Zaiane. A framework for analyzing dynamic social networks. In Proceedings of the 7th Conference on Applications of Social Network Analysis, ASNA ’10, 2010.Mansoureh Takaffoli, Farzad Sangi, Justin Fagnan, and Osmar R. Zaiane. Community evolution mining in dynamic social networks. Procedia - Social and Behavioral Sciences, 22:49–58, 2011.Mansoureh Takaffoli, Farzad Sangi, Justin Fagnan, and Osmar R. Zaiane. Modec - modeling and detecting evolutions of communities. In 5th International AAAI Conference on Weblogs and Social Media, ICWSM ’11, 2011.Mansoureh Takaffoli, Farzad Sangi, Justin Fagnan, and Osmar R. Zaiane. Tracking changes in dynamic information networks. In International Conference on Computational Aspects of Social Networks, CASoN, 2011.

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