Computer Science

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).

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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
[1]WANG S H.Community Detection in Signed Networks withGame Theory[J].Computer Science,2020,47(S2):459-463.
[2]ZHANG D K,YIN J,ZHU X Q,et al.Network Representation Learning:A Survey[J].IEEE Transactions on Big Data,2017,6(1):3-28.
[3]TIAN F,GAO B,CUI Q,et al.Learning deep representationsfor graph clustering[C]//The Twenty-Eighth AAAI Confe-rence on Artificial Intelligence.2014:1293-1299.
[4]YANG L,CAO X,HE D,et al.Modularity based community detection with deep learning[C]//International Joint Conference on Artificial Intelligence.2016.
[5]JIN D,GE M,LI Z,et al.Using Deep Learning for Community Discovery in Social Networks[C]//2017 IEEE 29th InternationalConference on Tools with Artificial Intelligence.2017.
[6]HU P,NIU Z,HE T,et al.Learning Deep Representations inLarge Integrated Network for Graph Clustering[C]//2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering.2018:101-105.
[7]WU L,ZHANG Q,CHEN C H,et al.Deep Learning Tech-niques for Community Detection in Social Networks[J].IEEE Access,2020(8):96016-96026.
[8]CAO J,JIN D,YANG L,et al.Incorporating network structure with node contents for community detection on large networks using deep learning[J].Neurocomputing,2018,297:71-81.
[9]CAO J,JIN D,DANG J.Autoencoder Based Community Detection with Adaptive Integration of Network Topology and Node Contents[C]//International Conference on Knowledge Science,Engineering and Management.Cham:Springer,2018.
[10]KRLJ B,KRALJ J,LAVRA N.Embedding-based SilhouetteCommunity Detection[J].Machine Learning,2020,109(1):161-219.
[11]REN W,YAN G,LIAO X,et al.Simple probabilistic algorithm for detecting community structure[J].Physical Review E,2009,79(2):036111.
[12]NEWMAN M E J.Modularity and community structure in networks[J].Proceedings of the National Academy of Sciences of the United States of America,2006,103(23):8577-8582.
[13]WANG R S,ZHANG S,WANG Y,et al.Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures[J].Neurocomputing,2008,72(1/2/3):134-141.
[14]PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:online learn-ing of social representations[C]//The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2014:701-710.
[15]LANCICHINETTI A,FOURTUNATO S.Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities[J].Physical Review E Statistical Nonlinear & Soft Matter Physics,2009,80(2):016118.
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