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| Vector Influence Clustering Coefficient Based Efficient Directed Community Detection Algorithm |
| DENG Xiaolong① ZHAI Jiayu② YIN Luanyu③ |
①(Key Laboratory of Trustworthy Distributed Computing and Service of Education Ministry, Beijing University of Posts and Telecommunications, Beijing 100876,China)
②(International School, Beijing University of Posts and Telecommunications, Beijing 100876, China)
③(Academy of Social Management, Beijing Normal University, Beijing 100875, China) |
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Abstract Community detection method is significant to character statistics of complex network. Community detection in directed structured network is an attractive research problem while most previous approaches attempt to divide undirected networks into communities while there has appeared many large scale directed social network such as WeChat circle of friends and Sina Micro-Blog. To solve the problem that low quality of model, low efficiency of execution and high deviation of precision from the conventional community detection algorithm on large-scale social network and directed network, this paper provides an approach that starts with the triangle structure of community basis and models the local information transfer to detect community in large-scale directed social network. Basing on the directed vector theory in probability graph and the high information transfer gain of vertex in directed network, this paper constructs the Information Transfer Gain (ITG) method and the corresponding target functions for evaluating the quality of a specific partition in community detection algorithm. Then the combine of ITG with the target function to compose the new community detection algorithm for directed network. Extensive experiments in synthetic signed network and real-life large networks derived from online social media, it is proved that the proposed method is more accurate and faster than several traditional community detection methods such as FastGN, OSLOM and Infomap.
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Received: 25 January 2017
Published: 18 August 2017
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| Fund:The National 973 Project of China (2013CB329600), The Philosophy and Social Science Project of Education Ministry (15JZD027), The National Culture Support Foundation Project of China (2013BAH43F01) |
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Corresponding Authors:
DENG Xiaolong
E-mail: shannondeng@bupt.edu.cn
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