Mining overlapping and hierarchical communities in complex networks
Mining overlapping and hierarchical communities in complex networks
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DOI：
10.1016/j.physa.2014.11.023
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发表时间：
2015-03
影响因子：
3.3
通讯作者：
Zhiwei Zhang;Zhenyu Wang
中科院分区：
文献类型：
--
作者：
Zhiwei Zhang;Zhenyu Wang
社会网络中的社区发现是社会计算的重要任务之一。高度相关的研究表明，社会网络通常既包含重叠结构，也包含层次结构。本文介绍了一种高效、实用的社区检测算法MOCC，该算法能够同时发现复杂网络中的重叠组织和层次组织。该算法首先从原始复杂网络中提取出所有最大团。通过使用MOCC中提出的聚集框架，将所有提取的最大团合并到树状图中。最后对树状图进行剖分，得到扩展分区密度最大的网络分区。利用计算机生成的人工网络和真实社会基准网络的实验结果给出了令人满意的对应关系。
Community detection in the social networks is one of the most important tasks of social computing. Highly relevant researches indicate that the social network generally contains both an overlapping and hierarchical structure. This paper introduces an efficient and functional community detection algorithm MOHCC, which can concurrently discover overlapping and hierarchical organization in complex networks. This algorithm first extracts all maximal cliques from the original complex network. Merges all extracted maximal cliques into a dendrogram by using the aggregative framework presented in MOHCC. Finally, it cuts through the dendrogram and obtains a network partition with maximum extended partition density. Experimental results utilizing computer-generated artificial networks and real-world social benchmark networks give satisfactory correspondence.