Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 198-203.doi: 10.11896/jsjkx.210200113

• Big Data & Data Science • Previous Articles     Next Articles

Deep Community Detection Algorithm Based on Network Representation Learning

PAN Yu1,2, ZOU Jun-hua1, WANG Shuai-hui3, HU Gu-yu1, PAN Zhi-song1   

  1. 1 School of Command and Control Engineering,Army Engineering University,Nanjing 210007,China
    2 The 31436 Unit of the Chinese People's Liberation Army,Shenyang 110000,China
    3 The Third Flight Training Base of Naval Aeronautical University,Qinhuangdao,Hebei 066000,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:PAN Yu,born in 1990,doctor.Her main research interests include data processing and mining in social networks and machine learning.
    PAN Zhi-song,born in 1973.Ph.D,professor,Ph.D supervisor.His main research interests include computer vision and machine learning.
  • Supported by:
    National Natural Science Foundation of China(62076251).

Abstract: Mining the community structure in the complex network is helpful to understand the internal structure and functional characteristics of the network,which has important theoretical value and significant practical significance.With the rapid development of information technology,the explosive growth of network data poses an unprecedented challenge for community detection.In this paper,the deep neural network is utilized to connect network representation learning and community detection domains,and a deep community detection method based on network representation learning is proposed.Firstly,the structural closeness of nodes is quantified according to their potential community membership similarities,and then a novel community structure method is proposed to construct the community structure matrix.Furthermore,a deep autoencoder that has several layers with non-linear functions is developed.The community structure matrix is used as the input of the deep autoencoder to obtain the low-dimension representation of the nodes which preserve the potential community structure.Finally,the K-means clustering strategy is applied to the network representation to obtain the community structure.Extensive experiments on both synthetic and real-world datasets of different scales demonstrate that the proposed method is feasible and effective.

Key words: Autoencoder, Community detection, Complex network, Deep neural network, Network representation learning

CLC Number: 

  • TP393
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