Community detection using global and local structural information
Community detection using global and local structural information
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DOI：
10.1007/s12043-012-0359-5
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Haipeng Yan;Ju Xiang;Xiaoyan Zhang;JUN-FENG Fan;Fang-Yao Chen;G. Fu;ER-MIN Guo;XIN-GUANG Hu;K. Hu;RU-MIN Wang
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文献类型：
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作者：
Haipeng Yan;Ju Xiang;Xiaoyan Zhang;JUN-FENG Fan;Fang-Yao Chen;G. Fu;ER-MIN Guo;XIN-GUANG Hu;K. Hu;RU-MIN Wang
社区检测对于理解复杂网络的结构和功能非常重要。在本文中，我们介绍了使用全局和局部结构信息的社区检测算法的一般过程，其中分别使用基于局部随机游走动力学和局部循环结构的边缘介数和局部相似性度量。这些算法在人工和现实网络上进行了测试。结果清楚地表明，所有算法在测试中都具有优异的性能，并且基于局部随机游走动力学的局部相似性度量优于基于局部循环结构的局部相似性度量。
Community detection is of considerable importance for understanding both the structure and function of complex networks. In this paper, we introduced the general procedure of the community detection algorithms using global and local structural information, where the edge betweenness and the local similarity measures respectively based on local random walk dynamics and local cyclic structures were used. The algorithms were tested on artificial and real-world networks. The results clearly show that all the algorithms have excellent performance in the tests and the local similarity measure based on local random walk dynamics is superior to that based on local cyclic structures.