Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (5): 41-50    DOI: 10.11925/infotech.2096-3467.2020.1208
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Fuzzy Overlapping Community Detection Algorithm Based on Node Vector Representation
Chen Wenjie(),Wen Yi,Yang Ning
Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
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Abstract

[Objective] This paper proposed a fuzzy community partition algorithm based on node vector representation,aiming to solve the problems of poor efficiency and accuracy of existing fuzzy overlapping community partition algorithms. [Methods] Firstly, the random walk strategy guided by node importance is used to generate the walk sequence, and then the skip-gram model is used to train the node vector. Then, the Gaussian mixture model is introduced into the community partition to realize the multi peak node data fitting. Finally, the optimal number of communities is obtained by maximizing the modularity. [Results] Compared with the classical community detection method, the EQ values of the algorithm on the real network jazz and artificial network N1 (mu = 0.5) are increased by 7.0% and 9.7% respectively, which can more accurately detect the community structure in the network. [Limitations] In the vector representation learning, only the topological structure information of complex network is considered, while the node attribute information and edge label information are ignored. [Conclusions] The fuzzy overlapping community detection algorithm based on node vector representation can effectively complete the community division task of complex network.

Received: 03 December 2020      Published: 27 May 2021
 ZTFLH: TP393
Fund:The work is supported by the 13th Five-year Informatization Plan of Chinese Academy of Sciences(XXH13506)
Corresponding Authors: Chen Wenjie     E-mail: chenwj@clas.ac.cn