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Community Mining: Discovering Communities in Social Networks Open Access


Other title
Community Mining
Social Network Analysis
Type of item
Degree grantor
University of Alberta
Author or creator
Chen, Jiyang
Supervisor and department
Goebel, Randy (Computing Science)
Zaiane, Osmar (Computing Science)
Examining committee member and department
Stroulia, Eleni (Computing Science)
Ester, Martin (Simon Fraser University)
Lin, Guohui (Computing Science)
Shiri, Ali (School of Library and Information Studies)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure, which means that there exists densely connected groups of vertices, with only sparser connections between groups. The main goal of community mining is to discover these communities in social networks or other similar information network environments. We face many deficiencies in current community structure discovery methods. First, one similarity metric is typically applied in all networks, without considering the differences in network and application characteristics. Second, many existing methods assume the network information is fully available, and one node only belongs to one cluster. However, in reality, a social network can be huge thus it is hard to access the complete network. It is also common for social entities to belong to multiple communities. Finally, relations between entities are hard to understand in heterogeneous social networks, where multiple types of relations and entities exist. Therefore, the thesis of this research is to tackle these community mining problems, in order to discover and evaluate community structures in social networks from various aspects.
License granted by Jiyang Chen ( on 2010-04-16 (GMT): 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. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein 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.
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