Discovering Temporal Communities from Social Network Documents

Discovering communities from documents involved in social discourse is an important topic in social network analysis, enabling greater understanding of the relationships among actors within a social network as well as topical trends in communication. This paper studies the discovery of communities from communication documents produced over time, including the discovery of temporal trends in community memberships. We first formulate static community discovery at a single time period as a tripartite graph partitioning problem. Then we propose to discover the temporal communities by threading the statically derived communities in different time periods using a new constrained partitioning algorithm, which partitions graphs based on topology as well as prior information regarding vertex membership. We evaluate the proposed approach on synthetic datasets and a real-world dataset prepared from the CiteSeer computer science research corpus. Quantitative evaluation on synthetic data demonstr...

• Type: Conference
• Year: 2007
• Where: ICDM
• Authors: Ding Zhou, Isaac G. Councill, Hongyuan Zha, C. Lee Giles
• Keywords: Community Discovery, Data Mining, ICDM 2007, Static Community Discovery, Time Periods