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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1151 |
| Date | 11 1900 |
| Creators | Chen, Jiyang |
| Contributors | Goebel, Randy (Computing Science), Zaiane, Osmar (Computing Science), Ester, Martin (Simon Fraser University), Shiri, Ali (School of Library and Information Studies), Lin, Guohui (Computing Science), Stroulia, Eleni (Computing Science) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 1623039 bytes, application/pdf |
| Relation | Jiyang Chen, Osmar R. Zaiane and Randy Goebel, Local Community Identification in Social Networks, International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Athens, Greece, July 20-22, 2009., Jiyang Chen, Osmar R. Zaiane and Randy Goebel, Detecting Communities in Social Networks using Local Information. Accepted as book chapters in Social Networks Analysis and Mining: foundations and applications, by Springer Verlag., Jiyang Chen, Osmar R. Zaiane and Randy Goebel, A Visual Data Mining Approach to Find Overlapping Communities in Networks, International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Athens, Greece, July 20-22, 2009., Jiyang Chen, Osmar R. Zaiane, Joerg Sander and Randy Goebel, ONDOCS: Ordering Nodes to Detect Overlapping Community Structure, Annuals of Information Systems, Special Issues on Data Mining for Social Network Data (in press)., Jiyang Chen, Osmar R. Zaiane and Randy Goebel, Detecting Communities in Large Networks by Iterative Local Expansion, International Conference on Computational Aspects of Social Networks (CASoN), Fontainebleau, France, June 24-27, 2009., Jiyang Chen, Osmar R. Zaiane and Randy Goebel, Detecting Communities in Social Networks using Max-Min Modularity, SIAM International Conference on Data Mining (SDM'09), Sparks, Nevada, USA, April 30-May 2, 2009., Jiyang Chen, Osmar R. Zaiane and Randy Goebel, An Unsupervised Approach to Cluster Web Search Results based on Word Sense Communities, IEEE/WIC/ACM Conferences on Web Intelligence (WI'08), Sydney, Australia, December 9-12, 2008., Jiyang Chen, Osmar R. Zaiane and Randy Goebel, Web Search Result Categorization based on Query Sense Communities, submitted to WWW journal., Osmar R. Zaiane, Jiyang Chen, and Randy Goebel, Mining Research Communities in Bibliographical Data, Chapter in Advances in Web Mining and Web Usage Analysis, published by Springer Verlag, 2008., Osmar R. Zaiane, Jiyang Chen, Randy Goebel, DBConnect: Mining Research Community on DBLP Data, Web Mining and Social Network Analysis Workshop in conjunction with ACM SIGKDD conference, pp 74-81, 12 August 2007 - San Jose, USA. |
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