计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 56-62.doi: 10.11896/jsjkx.220100155

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于马尔可夫相似性增强和网络嵌入的社区发现

曾祥宇, 龙海霞, 杨旭华   

  1. 浙江工业大学计算机科学与技术学院 杭州 310023
  • 收稿日期:2022-01-16 修回日期:2022-06-08 出版日期:2023-04-15 发布日期:2023-04-06
  • 通讯作者: 杨旭华(xhyang@zjut.edu.cn)
  • 作者简介:(zxyu15947@163.com)
  • 基金资助:
    国家自然科学基金(62176236,62106225)

Community Detection Based on Markov Similarity Enhancement and Network Embedding

ZENG Xiangyu, LONG Haixia, YANG Xuhua   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2022-01-16 Revised:2022-06-08 Online:2023-04-15 Published:2023-04-06
  • About author:ZENG Xiangyu,born in 1998,postgra-duate.His main research interests include community detection and graph neural network.
    YANG Xuhua,born in 1971,Ph.D,professor,is a member of China Computer Federation senior.His main research interests include machine learning and network science.
  • Supported by:
    National Natural Science Foundation of China(62176236,62106225).

摘要: 社区结构普遍存在于自然界的各种复杂网络中,是网络结构的重要特征之一。社区发现算法可以识别网络中的有用信息,有助于分析网络的结构和功能,被广泛应用于社交网络、生物和医学等领域。文中针对目前基于局部相似性的复杂网络社区发现算法精确度不高的问题,提出了一种基于马尔可夫相似性增强和网络嵌入的社区发现算法。首先,受马尔可夫链思想启发,提出了一种马尔可夫相似性增强方法,通过对初始网络的马尔可夫迭代状态进行转移,来获取稳态的马尔可夫相似性增强矩阵,根据马尔可夫相似性指标对网络进行初始的社区划分。然后结合网络的拓扑结构和网络嵌入,提出了一种新的社区相似性指标,将初始社区结构中的小社区与其连接紧密的社区合并,得到网络社区结构。在7个真实网络和可变参数的人工网络上,通过与其他5个知名社区发现算法的比较,证明了所提算法具有良好的社区发现效果。

关键词: 社区发现, 复杂网络, 马尔可夫相似性, 网络嵌入, 社区相似性

Abstract: Community structure is ubiquitous in various complex networks in nature and is one of the important characteristics of network structure.Community detection can identify useful information in the network,and help to analyze the structure and function of the network.It is widely used in social networks,biology,medicine and other fields.Aiming at the low accuracy of the current community detection algorithm based on local similarity in complex networks,a community detection algorithm based on Markov similarity enhancement and network embedding is proposed.Firstly,inspired by the idea of Markov chain,a Markov similarity enhancement method is proposed,which obtains the steady-state Markov similarity enhancement matrix through the Mar-kov iterative state transition of the initial network.According to the Markov similarity index,the network is divided into initial community structure.Then,a new community similarity index is proposed by combining the network topology and network embedding.The small community in the initial community structure is merged with its closely connected community to obtain the network community structure.On 7 real networks and artificial networks with variable parameters,compared with other 5 well-known community detection algorithms,it is proved that the proposed algorithm has a good community detection effect.

Key words: Community detection, Complex network, Markov similarity, Network embedding, Community similarity

中图分类号: 

  • TP391
[1]LI M,LU S,ZHANG L,et al.A Community Detection Method for Social Network Based on Community Embedding[J].IEEE Transactions on Computational Social Systems,2021,8(2):308-318.
[2]ZHANG W,SHANG R,JIAO L.Complex Network Graph Embedding Method Based on Shortest Path and MOEA/D for Community Detection[J].Applied Soft Computing,2020,97:106764.
[3]ACMAN M,DORP L V,SANTINI J M,et al.Large-scale Network Analysis Captures Biological Features of Bacterial Plasmids[J].Nature Communications,2020,11(1):1-11.
[4]HAN N,QIAO S J,YUAN C A,et al.Fast Community Parallel Detection Algorithm in Mobile Social Networks[J].Journal of Chongqing University of Technology:Natural Science,2020,34(1):94-102.
[5]IMANE M,NADJET K.Community Detection UsingFireworks Optimization Algorithm[J].International Journal of Artificial Intelligence Tools,2019,28(3):1950010.
[6]RAGHAVAN U N,ALBERT R,KUMARA S.Near linear time algorithm to detect community structures in large-scale networks[J].Physical review E,2007,76(3):036106.
[7]CLAUSET A,NEWMAN M E J,MOORE C.Finding Commu-nity Structure in Very Large Networks[J].Physical Review E,2004,70(6):066111.
[8]HU F,ZHU Y,SHI Y,et al.An Algorithm Walktrap-SPM forDetecting Overlapping Community Structure[J].International Journal of Modern Physics B,2017,31(15):1750121.
[9]NEWMAN M E J,GIRVAN M.Finding and Evaluating Community Structure in Networks[J].Physical Review E,2004,69(2):026113.
[10]GIRVAN M,NEWMAN M E J.Community Structure in Social and Biological Networks[J].Proceedings of the National Aca-demy of Sciences of the United States of America,2002,99(12):7821-7826.
[11]ROSVALL M,BERGSTROM C T.Maps of RandomWalks on Complex Networks Reveal Community Structure[J].Procee-dings of the National Academy of Sciences,2008,105(4):1118-1123.
[12]EUSTACE J,WANG X,CUI Y.Community Detection UsingLocal Neighborhood in Complex Networks[J].Physica A:Statistical Mechanics and its Applications,2015,436:665-667.
[13]LIU J L,WANG D L,ZHANG Y F,et al.Local Community DetectionApproach Based on Fuzzy Similarity Relation[J].Journal of Software,2020,31(11):3481-3491.
[14]KUMAR S,PANDA B S,AGGARWAL D.Community Detection in Complex Networks Using Network Embedding and Gravitational Search Algorithm[J].Journal of Intelligent Information Systems,2020,57(1):51-72.
[15]ZHAO X,LI X,ZHANG Z H,et al.Community Detection Algorithm Combining Community Embedding and Node Embedding[J].Computer Science,2020,47(10):121-125.
[16]HE D,WANG Y,CAO J,et al.A Network Embedding-en-hanced Bayesian Model for Generalized Community Detection in Complex Networks[J].Information Sciences,2021,575:306-322.
[17]WANG D,CUI P,ZHU W.Structural Deep Network Embed-ding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:1225-1234.
[18]WANG T,YIN L Y,WANG X X.A community DetectionMethod Based on Local Similarity and Degree Clustering Information[J].Physica A,2018,490:1344-1354.
[19]ZHANG J,DING X,YANG J.Revealing the Role of Node Similarity and Community Merging in Community Detection[J].Knowledge-Based Systems,2018,165:407-419.
[20]VERMA P,GOYAL R.Influence Propagation Based Community Detection in Complex Networks[J].Machine Learning with Applications,2020,12:100019.
[21]YOU X,MA Y,LIU Z.A Three-stage Algorithm on Communi-ty Detection in Social Networks[J].Knowledge-Based Systems,2020,187:104822.
[22]PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:OnlineLearning of Social Representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2014:701-710.
[23]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient Estimation of Word Representations in Vector Space[J].arXiv:1301.3781,2013.
[24]GROVER A,LESKOVEC J.Node2vec:Scalable Feature Lear-ning for Networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:855-864.
[25]TANG J,QU M,WANG M,et al.Line:Large-scale Information Network Embedding[C]//Proceedings of the 24th International Conference on World Wide Web.2015:1067-1077.
[26]NEWMAN M E J.Fast Algorithm for Detecting CommunityStructure in Networks[J].Physical Review E,2004,69(6):066133.
[27]NEWMAN M E J.Finding Community Structure in Networks Using the Eigenvectors of Matrices[J].Physical Review E,2006,74(3):036104.
[28]CLAUSET A,NEWMAN M E J,MOORE C.Finding Community Structure in Very Large Networks[J].Physical Review E,2004,70(6):066111.
[29]HESAMIPOUR S,BALAFAR M A.A New Method for Detecting Communities and their Centers Using the Adamic/Adar Index and Game Theory[J].Physica A:Statistical Mechanics and its Applications,2019,535:122354.
[30]SU Y S,LIU C L,NIU Y Y,et al.A Community Structure Enhancement-based Community Detection Algorithm for Complex Networks[J].IEEE Transactions on Systems Man Cybernetics-Systems,2021,51(5):2833-2846.
[31]SUN Z J,SUN Y N,CHANG X F,et al.Community Detection Based on the Matthew Effect[J].Knowledge-Based Systems,2020,205:106256.
[32]ZHANG L,PAN H B,SU Y S,et al.A Mixed RepresentationBased Multiobjective Evolutionary Algorithm for Overlapping Community Detection[J].IEEE Transactions on Cybernetics,2017,47(9):2703-2716.
[33]JIANG H,LIU Z,LIU C,et al.Community Detection in Complex Networks with an Ambiguous Structure Using Central Node Based Link Prediction[J].Knowledge-Based Systems,2020,195:105626.
[34]LIU H J,MA H F,ZHAO Q Q,et al.Target Community Detection with User Interest Preferences and Influence[J].Journal of Computer Research and Development,2021,58(1):70-82.
[35]LU H,SHEN Z,SANG X S,et al.Community Detection Me-thod Using Improved Density Peak Clustering and Nonnegative Matrix Factorization[J].Neorocomputing,2020,415:247-257.
[36]CHEN Y Z,SHI S,ZHU W P,et al.An Incremental Community Detection Algorithm Based on Neighborhood Following Relationship[J].Chinese Journal of Computers,2017,40(3):570-583.
[37]NATH K,SHANMUGAM R,VARADARANJAN V.Ma-CODE:A Multi-phase Approach on Community Detection in Evolving Networks[J].Information Sciences,2021,569:326-343.
[38]SAID A,ABBASI R A,MAQBOOL O,et al.CC-GA:A Clustering Coefficient Based Genetic Algorithm for Detecting Communities in Social Networks[J].Applied Soft Computing,2018,63:59-70.
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