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Minimum spanning trees for community detection
Authors:Jianshe Wu  Xiaoxiao Li  Licheng Jiao  Xiaohua Wang  Bo Sun
Affiliation:1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an 710071, PR China;2. Aeronautical Computing Technique Research Institute, Xi’an 710068, PR China;3. ZTE corporation, PR China
Abstract:A simple deterministic algorithm for community detection is provided by using two rounds of minimum spanning trees. By comparing the first round minimum spanning tree (1st-MST) with the second round spanning tree (2nd-MST) of the network, communities are detected and their overlapping nodes are also identified. To generate the two MSTs, a distance matrix is defined and computed from the adjacent matrix of the network. Compared with the resistance matrix or the communicability matrix used in community detection in the literature, the proposed distance matrix is very simple in computation. The proposed algorithm is tested on real world social networks, graphs which are failed by the modularity maximization, and the LFR benchmark graphs for community detection.
Keywords:Community structure   Minimum spanning tree   Distance matrix   Complex network
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