Hierarchical Community Detection Algorithm based on Local Similarity

Although community, one of the general characteristics of complex networks, has obvious hierarchical structure, in-depth research on its application in the current community detection algorithms is limited. In this paper we present a novel hierarchical community detection algorithm which starts from the node similarity calculation based on local adjacency in networks. Then we find the initial communities and joint them one by one as starting point from the bottom all the way to the top until all nodes fall within the same community. The concept of community similarity is defined for better community incorporation and each time, initial communities with the most similarity are integrated. Taking the advantage of time efficiency in local community mining, this kind of algorithm recognizes the community structure accurately when revealing the hierarchical structure of community, as the experiment shows.

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