Detecting community structure in complex networks based on a measure of information discrepancy |
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Authors: | Junhua Zhang Shihua Zhang Xiang-Sun Zhang |
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Affiliation: | a Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China b Graduate University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract: | Properties of complex networks, such as small-world property, power-law degree distribution, network transitivity, and network- community structure which seem to be common to many real-world networks have attracted great interest among researchers. In this study, global information of the networks is considered by defining the profile of any node based on the shortest paths between it and all the other nodes in the network; then a useful iterative procedure for community detection based on a measure of information discrepancy and the popular modular function Q is presented. The new iterative method does not need any prior knowledge about the community structure and can detect an appropriate number of communities, which can be hub communities or non-hub communities. The computational results of the method on real networks confirm its capability. |
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Keywords: | Complex network Community structure Measure of information discrepancy (MID) |
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