Improved community structure discovery algorithm based on combined clique percolation method and K-means algorithm
Research on the community structure of networks is beneficial for understanding the structure of networks, analyzing their characteristics and discovering the rules hidden in these networks. To address issues from previous community mining algorithms, such as the low rate of convergence and high tim...
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| Published in | Peer-to-peer networking and applications Vol. 13; no. 6; pp. 2224 - 2233 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.11.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1936-6442 1936-6450 |
| DOI | 10.1007/s12083-020-00902-9 |
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| Abstract | Research on the community structure of networks is beneficial for understanding the structure of networks, analyzing their characteristics and discovering the rules hidden in these networks. To address issues from previous community mining algorithms, such as the low rate of convergence and high time complexity, this study proposes an improved community structure discovery algorithm named CPMK-Means algorithm. The main idea of this algorithm can be summarised as follows. The clique percolation method (CPM) algorithm generates the maximum number of cliques by combining depth-first search with breadth-first search so that the number of cluster centres is determined. Then, the k centres are selected based on the principle of the maximum degree of centres and minimum similarity between different centres. Afterwards, nodes in the network are assigned to the communities formed by the k centres, and the iterations are performed repeatedly until the centres become stable. Finally, the overlapping communities are merged. Experiments are carried out on standard data sets Football and Collins to evaluate the performance of the CPMK-Means algorithm. Results indicate that the CPMK-Means algorithm can achieve better community mining and higher execution efficiency compared with other algorithms. Furthermore, it is superior to other algorithms in terms of precision, recall, accuracy, F-measure and separation. |
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| AbstractList | Research on the community structure of networks is beneficial for understanding the structure of networks, analyzing their characteristics and discovering the rules hidden in these networks. To address issues from previous community mining algorithms, such as the low rate of convergence and high time complexity, this study proposes an improved community structure discovery algorithm named CPMK-Means algorithm. The main idea of this algorithm can be summarised as follows. The clique percolation method (CPM) algorithm generates the maximum number of cliques by combining depth-first search with breadth-first search so that the number of cluster centres is determined. Then, the k centres are selected based on the principle of the maximum degree of centres and minimum similarity between different centres. Afterwards, nodes in the network are assigned to the communities formed by the k centres, and the iterations are performed repeatedly until the centres become stable. Finally, the overlapping communities are merged. Experiments are carried out on standard data sets Football and Collins to evaluate the performance of the CPMK-Means algorithm. Results indicate that the CPMK-Means algorithm can achieve better community mining and higher execution efficiency compared with other algorithms. Furthermore, it is superior to other algorithms in terms of precision, recall, accuracy, F-measure and separation. |
| Author | Zhou, Zhou Deng, WeiHong Xiao, Zhuopeng |
| Author_xml | – sequence: 1 givenname: Zhou surname: Zhou fullname: Zhou, Zhou organization: Department of Mathematics and Computer Science, Changsha University – sequence: 2 givenname: Zhuopeng surname: Xiao fullname: Xiao, Zhuopeng organization: Information Engineering Department, Zhangjiajie Institute of Aeronautical Engineering – sequence: 3 givenname: WeiHong surname: Deng fullname: Deng, WeiHong email: teddy_mail@126.com organization: Information Engineering Department, Zhangjiajie Institute of Aeronautical Engineering |
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| Cites_doi | 10.1145/2488388.2488483 10.1103/PhysRevE.69.066133 10.1016/j.jcss.2013.03.012 10.1007/s11280-009-0060-x 10.1016/j.knosys.2016.07.007 10.1109/ICDM.2012.139 10.1007/s11227-016-1790-z 10.1038/nature03607 10.1016/j.datak.2013.05.004 10.1145/2566486.2568010 10.1109/TKDE.2016.2518687 10.1109/TCCN.2018.2828854 10.1073/pnas.122653799 |
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| References | Chih-HsiuZKwang-ChengCSocial network analysis facilitates cognition in large wireless networks: clustering coefficient aided geographical routingIEEE Trans Cogn Commun Netw20184361863410.1109/TCCN.2018.2828854 PallaGDerényiIFarkasIVicsekTUncovering the overlapping community structure of complex networks in nature and societyNature2005435704381481810.1038/nature03607 HoweBHoweBHoweBScalable and efficient flow-based community detection for large-scale graph analysisACM Trans Knowl Discov Data20171133262 GirvanMNewmanMEJCommunity structure in social and biological networksProc Natl Acad Sci U S A20021278217826190807310.1073/pnas.122653799 WuHGaoLDongJDetecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networksPLoS One201493113 WenXChenWNLinYA maximal clique based multi-objective evolutionary algorithm for overlapping community detectionIEEE Trans Evol Comput2017213363377 MoonSLeeJGKangMParallel community detection on large graphs with MapReduce and GraphChiData Knowl Eng201510411731 NewmanMEFast algorithm for detecting community structure in networksPhys Rev E Stat Nonlinear Soft Matter Phys200369610.1103/PhysRevE.69.066133 MeoPDFerraraEFiumaraGMixing local and global information for community detection in large networksJ Comput Syst Sci20138017287310590910.1016/j.jcss.2013.03.012 ShiCCaiYFuDA link clustering based overlapping community detection algorithmData Knowl Eng201387939440410.1016/j.datak.2013.05.004 WangQDaiHNWuDData analysis on video streaming QoE over mobile networksEURASIP J Wirel Commun Netw20182018173110 Hollocou A, Maudet J, Bonald T et al (2017) A linear streaming algorithm for community detection in very large networks. In: Proceedings of Knowledge Discovery and Data Mining (KDD’ 2017), Halifax-Canada, pp 1–9 XuYXuHZhangDFinding overlapping community from social networks based on community forest modelKnowl-Based Syst2016109123825510.1016/j.knosys.2016.07.007 Arnau P, Dominguez-Sal D, Larriba-Pey JL (2014) High quality, scalable and parallel community detection for large real graphs. In: Proceedings of the 23rd International Conference on World Wide Web. ACM, pp 225–236. https://doi.org/10.1145/2566486.2568010 WhangJJGleichDFDhillonISOverlapping community detection using neighborhood-inflated seed expansionIEEE Trans Knowl Data Eng20162851272128410.1109/TKDE.2016.2518687 SalehanMKimDJKooCA study of the effect of social trust, trust in social networking services, and sharing attitude, on two dimensions of personal information sharing behaviorJ Supercomput20187483596361910.1007/s11227-016-1790-z WangZXLiZCDingXFOverlapping community detection based on node location analysisKnowl-Based Syst20161051225235 Statistic on the number of monthly active Facebook users. http://www.facebook.com WeiFQianWWangCDetecting overlapping community structures in networksWorld Wide Web Internet Web Inf Syst200912223526110.1007/s11280-009-0060-x Yang J, Leskovec J (2013) Community-affiliation graph model for overlapping network community detection. In: IEEE 12th International Conference on Data Mining. IEEE, pp 1170–1175. https://doi.org/10.1109/ICDM.2012.139 Ruan Y, Fuhry D, Parthasarathy S (2013) Efficient community detection in large networks using content and links. In: Proceedings of the 22nd International Conference on World Wide Web. ACM, pp 1089–1098. https://doi.org/10.1145/2488388.2488483 JJ Whang (902_CR11) 2016; 28 B Howe (902_CR20) 2017; 11 C Shi (902_CR9) 2013; 87 902_CR19 902_CR17 ME Newman (902_CR7) 2003; 69 902_CR1 902_CR10 Y Xu (902_CR12) 2016; 109 ZX Wang (902_CR13) 2016; 105 M Girvan (902_CR4) 2002; 12 902_CR15 F Wei (902_CR8) 2009; 12 M Salehan (902_CR5) 2018; 74 Q Wang (902_CR3) 2018; 2018 G Palla (902_CR6) 2005; 435 PD Meo (902_CR16) 2013; 80 H Wu (902_CR21) 2014; 9 S Moon (902_CR18) 2015; 104 Z Chih-Hsiu (902_CR2) 2018; 4 X Wen (902_CR14) 2017; 21 |
| References_xml | – reference: MeoPDFerraraEFiumaraGMixing local and global information for community detection in large networksJ Comput Syst Sci20138017287310590910.1016/j.jcss.2013.03.012 – reference: MoonSLeeJGKangMParallel community detection on large graphs with MapReduce and GraphChiData Knowl Eng201510411731 – reference: GirvanMNewmanMEJCommunity structure in social and biological networksProc Natl Acad Sci U S A20021278217826190807310.1073/pnas.122653799 – reference: Chih-HsiuZKwang-ChengCSocial network analysis facilitates cognition in large wireless networks: clustering coefficient aided geographical routingIEEE Trans Cogn Commun Netw20184361863410.1109/TCCN.2018.2828854 – reference: WangZXLiZCDingXFOverlapping community detection based on node location analysisKnowl-Based Syst20161051225235 – reference: WeiFQianWWangCDetecting overlapping community structures in networksWorld Wide Web Internet Web Inf Syst200912223526110.1007/s11280-009-0060-x – reference: WuHGaoLDongJDetecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networksPLoS One201493113 – reference: NewmanMEFast algorithm for detecting community structure in networksPhys Rev E Stat Nonlinear Soft Matter Phys200369610.1103/PhysRevE.69.066133 – reference: Yang J, Leskovec J (2013) Community-affiliation graph model for overlapping network community detection. In: IEEE 12th International Conference on Data Mining. IEEE, pp 1170–1175. https://doi.org/10.1109/ICDM.2012.139 – reference: Arnau P, Dominguez-Sal D, Larriba-Pey JL (2014) High quality, scalable and parallel community detection for large real graphs. In: Proceedings of the 23rd International Conference on World Wide Web. ACM, pp 225–236. https://doi.org/10.1145/2566486.2568010 – reference: WenXChenWNLinYA maximal clique based multi-objective evolutionary algorithm for overlapping community detectionIEEE Trans Evol Comput2017213363377 – reference: ShiCCaiYFuDA link clustering based overlapping community detection algorithmData Knowl Eng201387939440410.1016/j.datak.2013.05.004 – reference: PallaGDerényiIFarkasIVicsekTUncovering the overlapping community structure of complex networks in nature and societyNature2005435704381481810.1038/nature03607 – reference: Hollocou A, Maudet J, Bonald T et al (2017) A linear streaming algorithm for community detection in very large networks. In: Proceedings of Knowledge Discovery and Data Mining (KDD’ 2017), Halifax-Canada, pp 1–9 – reference: Statistic on the number of monthly active Facebook users. http://www.facebook.com – reference: WangQDaiHNWuDData analysis on video streaming QoE over mobile networksEURASIP J Wirel Commun Netw20182018173110 – reference: XuYXuHZhangDFinding overlapping community from social networks based on community forest modelKnowl-Based Syst2016109123825510.1016/j.knosys.2016.07.007 – reference: Ruan Y, Fuhry D, Parthasarathy S (2013) Efficient community detection in large networks using content and links. In: Proceedings of the 22nd International Conference on World Wide Web. ACM, pp 1089–1098. https://doi.org/10.1145/2488388.2488483 – reference: HoweBHoweBHoweBScalable and efficient flow-based community detection for large-scale graph analysisACM Trans Knowl Discov Data20171133262 – reference: SalehanMKimDJKooCA study of the effect of social trust, trust in social networking services, and sharing attitude, on two dimensions of personal information sharing behaviorJ Supercomput20187483596361910.1007/s11227-016-1790-z – reference: WhangJJGleichDFDhillonISOverlapping community detection using neighborhood-inflated seed expansionIEEE Trans Knowl Data Eng20162851272128410.1109/TKDE.2016.2518687 – volume: 9 start-page: 1 issue: 3 year: 2014 ident: 902_CR21 publication-title: PLoS One – ident: 902_CR15 doi: 10.1145/2488388.2488483 – volume: 69 issue: 6 year: 2003 ident: 902_CR7 publication-title: Phys Rev E Stat Nonlinear Soft Matter Phys doi: 10.1103/PhysRevE.69.066133 – volume: 80 start-page: 72 issue: 1 year: 2013 ident: 902_CR16 publication-title: J Comput Syst Sci doi: 10.1016/j.jcss.2013.03.012 – volume: 12 start-page: 235 issue: 2 year: 2009 ident: 902_CR8 publication-title: World Wide Web Internet Web Inf Syst doi: 10.1007/s11280-009-0060-x – volume: 109 start-page: 238 issue: 1 year: 2016 ident: 902_CR12 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2016.07.007 – volume: 2018 start-page: 1 issue: 173 year: 2018 ident: 902_CR3 publication-title: EURASIP J Wirel Commun Netw – ident: 902_CR10 doi: 10.1109/ICDM.2012.139 – volume: 21 start-page: 363 issue: 3 year: 2017 ident: 902_CR14 publication-title: IEEE Trans Evol Comput – volume: 74 start-page: 3596 issue: 8 year: 2018 ident: 902_CR5 publication-title: J Supercomput doi: 10.1007/s11227-016-1790-z – volume: 435 start-page: 814 issue: 7043 year: 2005 ident: 902_CR6 publication-title: Nature doi: 10.1038/nature03607 – volume: 11 start-page: 32 issue: 3 year: 2017 ident: 902_CR20 publication-title: ACM Trans Knowl Discov Data – volume: 87 start-page: 394 issue: 9 year: 2013 ident: 902_CR9 publication-title: Data Knowl Eng doi: 10.1016/j.datak.2013.05.004 – volume: 105 start-page: 225 issue: 1 year: 2016 ident: 902_CR13 publication-title: Knowl-Based Syst – ident: 902_CR17 doi: 10.1145/2566486.2568010 – volume: 104 start-page: 17 issue: 1 year: 2015 ident: 902_CR18 publication-title: Data Knowl Eng – ident: 902_CR19 – ident: 902_CR1 – volume: 28 start-page: 1272 issue: 5 year: 2016 ident: 902_CR11 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2016.2518687 – volume: 4 start-page: 618 issue: 3 year: 2018 ident: 902_CR2 publication-title: IEEE Trans Cogn Commun Netw doi: 10.1109/TCCN.2018.2828854 – volume: 12 start-page: 7821 year: 2002 ident: 902_CR4 publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.122653799 |
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