Community detection algorithm based on centrality and node distance in scale-free networks

Authors:
Sorn Jarukasemratana;Tsuyoshi Murata;Xin Liu
Affiliations:
Tokyo Institute of Technology, Ookayama, Meguro, Tokyo, Japan;Tokyo Institute of Technology, Ookayama, Meguro, Tokyo, Japan;Tokyo Institute of Technology, Ookayama, Meguro, Tokyo, Japan and CREST, JST, Gobancho, Chiyoda, Tokyo, Japan and Wuhan University of Technology, Wuhan, Hubei, China
Venue:
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Year:
2013

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Abstract

In this paper, we present a method for detecting community structures based on centrality value and node distance. Many real world networks possess a scale-free property and this property makes community detection difficult especially on algorithms that are based on modularity optimization. However, in our algorithm, communities are formed from hub nodes. Thus communities with scale-free property can be identified correctly. The method does not contain any random element, nor requires any pre-determined value such as the number of communities. Our experiments have shown that our algorithm is better than those based on modularity optimization in both real world and computer generated datasets.