标题：A novel framework in complex network analysis: Considering both structure of relations and individual characteristics in closeness centrality computation
期刊名称：International Journal of Industrial Engineering Computations
印刷版ISSN：1923-2926
电子版ISSN：1923-2934
出版年度：2013
卷号：4
期号：2
页码：227-240
DOI：10.5267/j.ijiec.2013.02.001
语种：English
出版社：Growing Science Publishing Company
摘要：In this paper, we develop a novel framework for defining radial measures of centrality in complex networks. This framework is based on the combination of two approaches: social network analysis and traditional social science approach by considering both structure of relations and individual characteristics. It is always an important issue to detect communities in complex networks as efficiently as possible to understand both the structure and function of the networks and to interpret radial centrality measures. Therefore, we propose spectral clustering by determining the best number of communities as a prerequisite stage before finding radial measures. Based on the proposed framework, an algorithm to compute the closeness centrality in complex networks is developed. We test the proposed algorithm on Zachary’s karate club network, which is considerably used as a benchmark for community detection in a network. The preliminary results indicate that the new method is efficient at detecting both good inter-cluster closeness centrality and the appropriate number of clusters.
关键词：Closeness centrality; Community structure; Complex networks; Node attribute; Social network; Spectral clustering
