Topic oriented community detection through social objects and link analysis in social networks

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• 作者：Zhongying Zhao^a ; ^b ; ^c ; ^{zy.zhao@siat.ac.cn} ; ^{zy.zhao10@gmail.com} ; Shengzhong Feng^b ; ^{sz.feng@siat.ac.cn} ; Qiang Wang^b ; ^{qiang.wang1@siat.ac.cn} ; Joshua Zhexue Huang^b ; ^{zx.huang@siat.ac.cn} ; Graham J. Williams^b ; ^{Graham.Williams@togaware.com} ; Jianping Fan^b ; ^{jp.fan@siat.ac.cn}
• 关键词：Social networks ; Community detection ; Link analysis ; Social objects clustering
• 刊名：Knowledge-Based Systems
• 出版年：2012
• 出版时间：February 2012
• 年：2012
• 卷：26
• 期：Complete
• 页码：164-173
• 全文大小：1756 K

文摘

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.