Community Detection in Social Networks Using Content and Link Analysis

被引：0

作者：

Kakisim, Arzu ^{[1
]}

Sogukpinar, Ibrahim ^{[1
]}

机构：

[1] Gebze Tekn Univ, Bilgisayar Muhendisligi Bolumu, Kocaeli, Turkey

来源：

2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2015年

关键词：

community detection; data mining; social networks;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Recently, community detection in social networks is studied as a major problem. Most existing methods solve the problem of community detection using link structure of networks. In this case, communities only reflect the topological features of network. Documents of social members in network are ignored. In this paper, hierarchical modularity maximization algorithm that is frequently used in literature is modified using similarities between members. Experiments on real data sets, proposed algorithm can achieve a better performance.

引用

页码：1521 / 1524

页数：4

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