Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 11-16.doi: 10.11896/jsjkx.210500151

• Intelligent Computing • Previous Articles     Next Articles

Survey of Graph Neural Network in Community Detection

NING Yi-xin, XIE Hui, JIANG Huo-wen   

  1. School of Mathematics and Computer Science,Jiangxi Science and Technology Normal University,Nanchang 330038,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:NING Yi-xin,born in 1997.Her main research interests include community detection and graph neural networks.
    XIE Hui,born in 1978,Ph.D,associate professor.His main research interests include data mining and intelligent re-commendation.
  • Supported by:
    National Natural Science Foundation of China (71561013,61762044),Social Science Planning Projects in Jiangxi Province (20TQ04),Fund of Humanities and Social Sciences in Universities of Jiangxi Province (JC17221,JD18083,JC18109) and Key Project of Science & Technology Plan by Education Department of Jiangxi Province(GJJ170661).

Abstract: Community structure is one of the universal topological properties in complex networks,and discovering community structure is the basic task of complex network analysis.The purpose of community detection is to divide the network into several substructures,which plays an important role in understanding the network and revealing its potential functions.Graph Neural Network (GNN) is a model for processing graph structure data,which has the advantage of feature extraction and representation from graph,and has become an important research field of artificial intelligence and big data.Network data is a typical graph structure data.Using graph neural network model to solve the problem of community detection is a new direction of community detection research.In this paper,we first discuss the GNN model,analyze the process of GNN community detection,and discuss the progress of existing GNN community detection and the direction of future research in detail from two aspects of overlapping community and non-overlapping community.

Key words: Community detection, Deep learning, Graph neural network, Non-overlapping community detection, Overlapping community detection

CLC Number: 

  • TP331
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