Hierarchical Agglomeration Community Detection Algorithm via Community Similarity Measures
نویسندگان
چکیده
Community detection is an important method for analyzing community structure of real-world networks. Most of the existing hierarchical agglomeration community detection algorithms have either high computational complexity or unsatisfied community detection results. In this contribution, we present a new hierarchical agglomeration community detection algorithm, called Community Detection Algorithm via Community Similarity Measures (CDACSM), It can effectively improve community detection results, and has lower computational complexity. Firstly, the proposed algorithm repeatedly joins communities by using single-node community measure and combination rule. Secondly, it adjusts a few nodes by SHARC, which is an advanced label propagation algorithm. Finally, it merges communities based on community similarity measure. The algorithm CDACSM is demonstrated with real-world and artificial networks, the experiment show that CDACSM has a more efficient and higher accurate result of community detection compared with some hierarchical algorithms recently proposed.
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