A Local Random Walk Method For Identifying Communities In Social Networks

被引:0
|
作者
Bahadori, Sondos [1 ]
Moradi, Parham [2 ]
机构
[1] Islamic Azad Univ, Sanandaj Branch, Dept Comp Engn, Sanandaj, Iran
[2] Univ Kurdistan, Dept Comp Engn, Sanandaj, Iran
来源
2017 ARTIFICIAL INTELLIGENCE AND ROBOTICS (IRANOPEN) | 2017年
关键词
complex network; community; node's feature set; CENTRALITY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Community detection is a substantial technique to find out the relationship between nodes in complex networks. By understanding the behavior of elements in a community, one can predict the overall feature of the large scale social network. Detecting different communities in large scale network is a challenging task due to huge data size associated with such network. The main purpose of this paper is finding distinct communities. For this reason, in this paper after using limited Random Walk to detect nodes feature set, nodes that share higher common feature set form a community. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover communities in the network.
引用
收藏
页码:177 / 181
页数:5
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