Computer Science ›› 2020, Vol. 47 ›› Issue (5): 72-78.doi: 10.11896/jsjkx.190400160

• Databωe & Big Data & Data Science • Previous Articles     Next Articles

Overlapping Community Detection Method Based on Rough Sets and Density Peaks

ZHANG Qin, CHEN Hong-mei, FENG Yun-fei   

  1. School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China
    Key Laboratory of Cloud Computing and Intelligent Technology,Southwest Jiaotong University,Chengdu 611756,China
  • Received:2019-04-18 Online:2020-05-15 Published:2020-05-19
  • About author:ZHANG Qin,born in 1995,postgradua-te,is a member of China Computer Fe-deration.Her main research interests include database technology and data mining.
    CHEN Hong-mei,born in 1971,Ph.D,professor,Ph.D supervisor,is a ember of China Computer Federation.Her main research interests include granular calculation,rough sets and intelligent information processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61572406).

Abstract: With the development of the Internet and society,a large number of interrelated and interdependent data is produced in various fields every day,which form various complex networks according to different themes.Mining community structure of complex networks is an important research content,which has extremely important significance in recommendation system,behavior prediction and information spreading.Moreover,overlapping community structure of complex networks exists universally in life,which has practical research significance.In order to detect overlapping communities effectively in complex networks,an overlapping community detection method OCDRD based on rough sets and density peaks is proposed in this paper,in which rough set theory is used to analyze communities and identify overlapping nodes.Firstly,the global similarities among network nodes are obtained by using grey correlation analysis method based on the traditional local similarity measure of network nodes.Then the global similarities among network nodes are converted to distance among nodes.The center nodes of the community are automatically selected by the network structure by applying the idea of density peaks based clustering.Next,the lower approximation,the upper approximation,and the boundary region of the community are defined according to the distance ratio relation among nodes in the network.Finally,the threshold value of distance ratio is adjusted iteratively,and the boundary region of the community is calculated repeatedly in each iteration until the optimal overlapping community structure is obtained.The OCDRD algorithm is compared with other community detection algorithms that have achieved good results in recent years both on LFR benchmark artificial network datasets and real network datasets.By analyzing two common community detection evaluation indexes,NMI and overlapping module degree EQ,the experimental results show that OCDRD algorithm is superior to other community detection algorithms in community partition structure andit is feasible and effective.

Key words: Density peaks, Grey correlation analysis method, Overlapping community detection, Rough set

CLC Number: 

  • TP391
[1]NEWMAN M E J.Networks:An Introduction[EB/OL].[2010-09].
[2]BAI X Y,YANG P L,SHI X H.An overlapping community detection algorithm based on density peaks[J].Neurocomputing,2017,226:7-15.
[3]WU H,GAO L,DONG J H,et al.Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks[J].PLoS One,2014,9(3):e91856.
[4]ZHANG X Y,WANG C T,SU Y S,et al.A fast overlapping community detection algorithm based on weak cliques for large-scale networks[J].IEEE Transactions on Computational Social Systems,2017,4(4):218-230.
[5]SUN H L,LIU J,HUANG J B,et al.LinkLPA:a link-based label propagation algorithm for overlapping community detection in networks[J].Computational Intelligence,2017,33(2):308-331.
[6]YU Z Y,CHEN J J,QUO K,et al.Overlapping community detection based on random walk and seeds extension[C]//Proceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing(ChineseCSCW '17).New York,USA:ACM Press,2017:18-24.
[7]BANSAL S,BHOWMICK S,PAYMAL P.Fast community detection for dynamic complex networks[M]//Communications in Computer and Information Science.Springer,2011:196-207.
[8]PAWLAK Z.Rough sets[J].International Journal of Computer &Information Sciences,1982,11(5):341-356.
[9]GRECO S,MATARAZZO B,SLOWINSKI R.Rough sets theory for multicriteria decision analysis[J].European Journal of Operational Research,2001,129(1):1-47.
[10]ZHANG W X,WU W Z.An introduction and a survey for the studies of rough set theory[J].Fuzzy Systems and Mathema-tics,2000,14(4):1-12.
[11]ZHANG Y,WU B,LIU Y.A novel community detection me-thod based on rough set K-means[J].Journal of Electronics and Information Technology,2017,39(4):770-777.
[12]ZHU W Q,FU Y C.Community structure detection algorithm based on rough set[J].Computer Engineering,2011,37(14):41-43.
[13]RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492-1496.
[14]DENG J L.The relational space in grey system theory[J].Fuzzy Mathematics,1985(2):1-10.
[15]NEWMAN M E J,MICHELLE G.Finding and evaluating community structure in networks[J].Physical Review E,2004,69(2):026113-026113.
[16]ZHOU X,LIU Y H,WANG J,et al.A density based link clustering algorithm for overlapping community detection in networks[J].Physica A:Statistical Mechanics and Its Applications,2017,486:65-78.
[17]XU X,YURUK N,FENG Z,et al.SCAN:a structural clustering algorithm for networks[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2007:824-833.
[18]LIU S F,CAI H,YANG Y J,et al.Advance in grey incidence analysis modelling[J].Systems Engineering-Theory & Practice,2013,33(8):2041-2046.
[19]ZHANG Q,QIU Q,GUO W,et al.A social community detection algorithm based on parallel grey label propagation[J].Computer Networks,2016,107(1):133-143.
[20]WANG M M,ZUO W,WANG Y.An improved density peaks-based clustering method for social circle discovery in social networks[J].Neurocomputing,2016,179:219-227.
[21]YANG Z,WANG H J,ZHOU Y.A clustering algorithm with adaptive cut-off distance and cluster centers[J].Data Analysis and Knowledge Discovery,2018(3):39-48.
[22]DWYER T,HONG S H,DIPK K,et al.Visual analysis of network centralities[C]//Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation.2006:189-197.
[23]BARTHELEMY M.[Lecture Notes in Morphogenesis] Mor-phogenesis of Spatial Networks‖Betweenness centrality[OL].
[24]SABIDUSSI G.The centrality index of a graph[J].Psychome-trika,1966,31(4):581-603.
[25]BONACICH P.Power and centrality:a family of measures[J].American Journal of Sociology,1987,92(5):1170-1182.
[26]DING J J,HE X X,YUAN J Q,et al.Community detection by propagating the label of center[J].Physica A:Statistical Mechanics and Its Applications,2018,503:675-686.
[27]HUANG L,WANG G S,WANG Y,et al.A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection[J].International Journal of Modern Physics B,2016,30(24):1650167.
[28]LANCICHINETTI A,FORTUNATO S.Community detection algorithms:a comparative analysis[J].Physical Review E,2009,80(5):056117.
[29]XIE J R,KELLEY S,SZYMANSKI B K.Overlapping community detection in networks[J].ACM Computing Surveys,2013,45(4):1-35.
[30]NEWMAN M.Network data[EB/OL].[2013-04-19].
[31]ZACHARY W W.An information flow model for conflict and fission in small groups[J].Journal of Anthropological Research,1977,33(4):452-473.
[32]LUSSEAU D,SCHNEIDER K,BOISSEAU O J,et al.The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations[J].Behavioral Ecology and Sociobiology,2003,54(4):396-405.
[33]KREBS V.Books about US Politics[EB/OL].
[34]GIRVAN M,NEWMAN M E J.Community structure in social and biological networks[EB/OL].
[35]STREHL A,GHOSH J.Cluster ensembles:a knowledge reuse framework for combining partitionings[C]//Eighteenth national conference on Artificial intelligence.2002:93-98.
[36]LANCICHINETTI A,FORTUNATO S,KERTÉSZ J.Detecting the overlapping and hierarchical community structure in complex networks[J].New Journal of Physics,2009,11(3):033015.
[37]NICOSIA V,MANGIONI G,CARCHIOLO V,et al.Extending the definition of modularity to directed graphs with overlapping communities[J].Journal of Statistical Mechanics:Theory and Experiment,2009(3):P03024.
[1] CHENG Fu-hao, XU Tai-hua, CHEN Jian-jun, SONG Jing-jing, YANG Xi-bei. Strongly Connected Components Mining Algorithm Based on k-step Search of Vertex Granule and Rough Set Theory [J]. Computer Science, 2022, 49(8): 97-107.
[2] XU Si-yu, QIN Ke-yun. Topological Properties of Fuzzy Rough Sets Based on Residuated Lattices [J]. Computer Science, 2022, 49(6A): 140-143.
[3] FANG Lian-hua, LIN Yu-mei, WU Wei-zhi. Optimal Scale Selection in Random Multi-scale Ordered Decision Systems [J]. Computer Science, 2022, 49(6): 172-179.
[4] CHEN Yu-si, AI Zhi-hua, ZHANG Qing-hua. Efficient Neighborhood Covering Model Based on Triangle Inequality Checkand Local Strategy [J]. Computer Science, 2022, 49(5): 152-158.
[5] SUN Lin, HUANG Miao-miao, XU Jiu-cheng. Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [J]. Computer Science, 2022, 49(4): 152-160.
[6] WANG Zi-yin, LI Lei-jun, MI Ju-sheng, LI Mei-zheng, XIE Bin. Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost [J]. Computer Science, 2022, 49(4): 161-167.
[7] WANG Zhi-cheng, GAO Can, XING Jin-ming. Three-way Approximate Reduction Based on Positive Region [J]. Computer Science, 2022, 49(4): 168-173.
[8] XUE Zhan-ao, HOU Hao-dong, SUN Bing-xin, YAO Shou-qian. Label-based Approach for Dynamic Updating Approximations in Incomplete Fuzzy Probabilistic Rough Sets over Two Universes [J]. Computer Science, 2022, 49(3): 255-262.
[9] CHEN Xiang-tao, ZHAO Mei-jie, YANG Mei. Overlapping Community Detection Algorithm Based on Subgraph Structure [J]. Computer Science, 2021, 48(9): 244-250.
[10] LI Yan, FAN Bin, GUO Jie, LIN Zi-yuan, ZHAO Zhao. Attribute Reduction Method Based on k-prototypes Clustering and Rough Sets [J]. Computer Science, 2021, 48(6A): 342-348.
[11] TANG Xin-yao, ZHANG Zheng-jun, CHU Jie, YAN Tao. Density Peaks Clustering Algorithm Based on Natural Nearest Neighbor [J]. Computer Science, 2021, 48(3): 151-157.
[12] NING Yi-xin, XIE Hui, JIANG Huo-wen. Survey of Graph Neural Network in Community Detection [J]. Computer Science, 2021, 48(11A): 11-16.
[13] XUE Zhan-ao, SUN Bing-xin, HOU Hao-dong, JING Meng-meng. Optimal Granulation Selection Method Based on Multi-granulation Rough Intuitionistic Hesitant Fuzzy Sets [J]. Computer Science, 2021, 48(10): 98-106.
[14] WANG Wei-dong, XU Jin-hui, ZHANG Zhi-feng, YANG Xi-bei. Gaussian Mixture Models Algorithm Based on Density Peaks Clustering [J]. Computer Science, 2021, 48(10): 191-196.
[15] ZHANG Yu, LU Yi-hong, HUANG De-cai. Weighted Hesitant Fuzzy Clustering Based on Density Peaks [J]. Computer Science, 2021, 48(1): 145-151.
Full text



No Suggested Reading articles found!