Affiliations: [a] The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China | [b] STTC, The Ministry of Science and Technology, Beijing, China
Correspondence:
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Corresponding author: Shu Zhao, The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China. E-mail: [email protected].
Abstract: Overlapping communities exist in real networks, where the communities represent hierarchical community structures, such as schools and government departments. A non-binary tree allows a vertex to belong to multiple communities to obtain a more realistic overlapping community structure. It is challenging to select appropriate leaf vertices and construct a hierarchical tree that considers a large amount of structural information. In this paper, we propose a non-binary hierarchical tree overlapping community detection based on multi-dimensional similarity. The multi-dimensional similarity fully considers the local structure characteristics between vertices to calculate the similarity between vertices. First, we construct a similarity matrix based on the first and second-order neighbor vertices and select a leaf vertex. Second, we expand the leaf vertex based on the principle of maximum community density and construct a non-binary tree. Finally, we choose the layer with the largest overlapping modularity as the result of community division. Experiments on real-world networks demonstrate that our proposed algorithm is superior to other representative algorithms in terms of the quality of overlapping community detection.
Keywords: Hierarchical and overlapping community detection, multi-dimensional similarity matrix, non-binary tree
