Topic oriented community detection through social objects and link analysis in social networks
Date
2012
Authors
Zhao, Zhongying
Feng, Shengzhong
Wang, Qiang
Huang, Joshua Zhexue
Williams, Graham
Fan, Jianping
Journal Title
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Volume Title
Publisher
Elsevier
Abstract
Community detection is an important issue in social network analysis. Most existing methods detect communities through analyzing the linkage of the network. The drawback is that each community identified by those methods can only reflect the strength of connections, but it cannot reflect the semantics such as the interesting topics shared by people. To address this problem, we propose a topic oriented community detection approach which combines both social objects clustering and link analysis. We first use a subspace clustering algorithm to group all the social objects into topics. Then we divide the members that are involved in those social objects into topical clusters, each corresponding to a distinct topic. In order to differentiate the strength of connections, we perform a link analysis on each topical cluster to detect the topical communities. Experiments on real data sets have shown that our approach was able to identify more meaningful communities. The quantitative evaluation indicated that our approach can achieve a better performance when the topics are at least as important as the links to the analysis.
Description
Keywords
Keywords: Community detection; Link analysis; Quantitative evaluation; Real data sets; Social Network Analysis; Social Networks; Social objects clustering; Population dynamics; Semantics; Social networking (online); Clustering algorithms Community detection; Link analysis; Social networks; Social objects clustering
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Source
Knowledge-Based Systems
Type
Journal article
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2037-12-31