Community detection in complex networks using density-based clustering algorithm and manifold learning
Like clustering analysis, community detection aims at assigning nodes in a network into different communities. Fdp is a recently proposed density-based clustering algorithm which does not need the number of clusters as prior input and the result is insensitive to its parameter. However, Fdp cannot b...
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| Published in: | Physica A Vol. 464; pp. 221 - 230 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.12.2016
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| ISSN: | 0378-4371, 1873-2119 |
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| Abstract | Like clustering analysis, community detection aims at assigning nodes in a network into different communities. Fdp is a recently proposed density-based clustering algorithm which does not need the number of clusters as prior input and the result is insensitive to its parameter. However, Fdp cannot be directly applied to community detection due to its inability to recognize the community centers in the network. To solve the problem, a new community detection method (named IsoFdp) is proposed in this paper. First, we use IsoMap technique to map the network data into a low dimensional manifold which can reveal diverse pair-wised similarity. Then Fdp is applied to detect the communities in the network. An improved partition density function is proposed to select the proper number of communities automatically. We test our method on both synthetic and real-world networks, and the results demonstrate the effectiveness of our algorithm over the state-of-the-art methods.
•A density-based clustering framework is proposed for community structure detection.•An improved partition density is proposed to evaluate the quality of the detected communities.•The framework is insensitive to its parameters, and easy to implement.•The comparisons performed on the synthetic benchmarks and the real-world networks show the effectiveness of the framework. |
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| AbstractList | Like clustering analysis, community detection aims at assigning nodes in a network into different communities. Fdp is a recently proposed density-based clustering algorithm which does not need the number of clusters as prior input and the result is insensitive to its parameter. However, Fdp cannot be directly applied to community detection due to its inability to recognize the community centers in the network. To solve the problem, a new community detection method (named IsoFdp) is proposed in this paper. First, we use IsoMap technique to map the network data into a low dimensional manifold which can reveal diverse pair-wised similarity. Then Fdp is applied to detect the communities in the network. An improved partition density function is proposed to select the proper number of communities automatically. We test our method on both synthetic and real-world networks, and the results demonstrate the effectiveness of our algorithm over the state-of-the-art methods.
•A density-based clustering framework is proposed for community structure detection.•An improved partition density is proposed to evaluate the quality of the detected communities.•The framework is insensitive to its parameters, and easy to implement.•The comparisons performed on the synthetic benchmarks and the real-world networks show the effectiveness of the framework. |
| Author | You, Tao Shia, Ben-Chang Zhang, Zhong-Yuan Ning, Yi-Zi Cheng, Hui-Min |
| Author_xml | – sequence: 1 givenname: Tao surname: You fullname: You, Tao organization: School of Statistics and Mathematics, Central University of Finance and Economics, Haidian District, Beijing 100081, China – sequence: 2 givenname: Hui-Min surname: Cheng fullname: Cheng, Hui-Min organization: School of Statistics and Mathematics, Central University of Finance and Economics, Haidian District, Beijing 100081, China – sequence: 3 givenname: Yi-Zi surname: Ning fullname: Ning, Yi-Zi organization: School of Statistics and Mathematics, Central University of Finance and Economics, Haidian District, Beijing 100081, China – sequence: 4 givenname: Ben-Chang surname: Shia fullname: Shia, Ben-Chang organization: Big Data Research Center & School of Management, School of Health Care Administration, Taipei Medical University, Taiwan – sequence: 5 givenname: Zhong-Yuan surname: Zhang fullname: Zhang, Zhong-Yuan email: zhyuanzh@gmail.com organization: School of Statistics and Mathematics, Central University of Finance and Economics, Haidian District, Beijing 100081, China |
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| Cites_doi | 10.1038/nphys2188 10.1145/1281192.1281280 10.1073/pnas.0706851105 10.1007/s10878-010-9356-0 10.1016/j.physa.2013.08.028 10.1016/j.physa.2013.05.039 10.1007/s00265-003-0651-y 10.1038/nature09182 10.1038/30918 10.1103/PhysRevE.66.066121 10.1103/PhysRevE.87.062803 10.1016/j.physrep.2009.11.002 10.1073/pnas.122653799 10.1016/j.jcss.2013.03.012 10.1016/j.physa.2005.04.022 10.1126/science.286.5439.509 10.1016/j.physa.2009.09.018 10.1142/S0129183115500965 10.1038/nphys1130 10.1073/pnas.0400054101 10.1016/j.physa.2006.07.023 10.1142/S0217984908016285 10.1016/j.physa.2010.11.027 10.1103/PhysRevE.81.046114 10.1103/PhysRevLett.100.078701 10.1142/S0219525903001067 10.1088/1742-5468/2015/01/P01001 10.1109/TKDE.2012.100 10.1126/science.290.5500.2319 10.1002/widm.30 10.1016/j.physa.2012.12.013 10.1103/PhysRevE.70.066111 10.1016/j.physa.2014.01.043 10.1126/science.1242072 |
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| Keywords | Community detection Manifold learning Density-based clustering IsoMap Complex network |
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| References | De Meo (br000155) 2014; 80 Zhang, Ahn (br000080) 2015; 26 Wang, Liu, Liu (br000160) 2013; 392 Huang (br000050) 2013; 25 Ma, Gao, Yong, Fu (br000085) 2010; 389 Zhang, Wang, Ahn (br000075) 2013; 87 Rodriguez, Laio (br000115) 2014; 344 Fortunato (br000090) 2010; 486 Caldarelli, Capocci, De Los Rios (br000145) 2002; 89 Finding Clusters of Different Sizes, Shapes, and Densities in Noisy High Dimensional Data, 2003. Wang, Liu, Liu (br000040) 2013; 392 Serrano, Boguñá (br000140) 2003; 68 Medus, Acuna, Dorso (br000030) 2005; 358 Girvan, Newman (br000205) 2002; 99 Zhang, Wang, Zhang (br000095) 2007; 374 M. Ester, H.P. Kriegel, J. Sander, et al. A density-based algorithm for discovering clusters in large spatial databases with noise, in: Published in Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, KDD-96. Ronhovde, Zohar (br000185) 2015; 2015 Barabsi (br000005) 2012; 8 Jiang, Caiyan, Jian (br000055) 2013; 392 Gong, Liu, Ma (br000100) 2014; 403 Gleiser, Danon (br000225) 2003; 6 Rosvall, Bergstrom (br000035) 2008; 105 Jin, Shuliang, Chenyang (br000045) 2013; 392 Knuth (br000215) 1993 Ronhovde, Zohar (br000180) 2010; 81 Söderberg (br000150) 2002; 66 V. De silva, J. Tenenbaum, Global versus local methods in nonlinear dimensionality reduction, in: Proceedings of the Conference on Advances in Neural Information Processing Systems, NIPS, 2003. Radicchi, Castellano, Cecconi (br000020) 2004; 101 Zhang, Li, Wang, Wang (br000065) 2012; 23 Tenenbaum, Silva, Langford (br000165) 2000; 290 Strehl, Ghosh (br000200) 2002; 3 Lusseau, Schneider, Boisseau (br000220) 2003; 54 Clauset, Newman (br000025) 2004; 70 Boguñá, Krioukov, Claffy (br000130) 2009; 1 Serrano, Krioukov, Boguñá (br000135) 2008; 100 Watts, Strogatz (br000010) 1998; 393 Lai, Hongtao (br000060) 2008; 22 Kriegel, Krõger, Sander (br000110) 2011; 1 Zhang (br000070) 2013; 56 Lancichinetti, Fortunato, Radicchi (br000210) 2008; 78 Barabsi, Albert (br000015) 1999; 286 Lu, Zhou (br000170) 2011; 390 Clustering sentences with density peaks for multi-document summarization., in: Rada Mihalcea; Joyce Yue Chai & Anoop Sarkar, ed., ’HLT-NAACL’, The Association for Computational Linguistics, pp. 1262–1267. X. Xu, N. Yuruk, Z.T. Feng, et al. Scan: a structural clustering algorithm for networks, in: Proceedings of the 13th ACM SIGKDD International, 2007, pp. 824–833. Ahn, Bagrow, Lehmann (br000175) 2010; 466 Zhang (10.1016/j.physa.2016.07.025_br000065) 2012; 23 Tenenbaum (10.1016/j.physa.2016.07.025_br000165) 2000; 290 Medus (10.1016/j.physa.2016.07.025_br000030) 2005; 358 10.1016/j.physa.2016.07.025_br000125 Wang (10.1016/j.physa.2016.07.025_br000160) 2013; 392 Clauset (10.1016/j.physa.2016.07.025_br000025) 2004; 70 10.1016/j.physa.2016.07.025_br000105 Gleiser (10.1016/j.physa.2016.07.025_br000225) 2003; 6 Lusseau (10.1016/j.physa.2016.07.025_br000220) 2003; 54 Barabsi (10.1016/j.physa.2016.07.025_br000015) 1999; 286 Ma (10.1016/j.physa.2016.07.025_br000085) 2010; 389 10.1016/j.physa.2016.07.025_br000120 Zhang (10.1016/j.physa.2016.07.025_br000070) 2013; 56 Ronhovde (10.1016/j.physa.2016.07.025_br000180) 2010; 81 Radicchi (10.1016/j.physa.2016.07.025_br000020) 2004; 101 Lai (10.1016/j.physa.2016.07.025_br000060) 2008; 22 Zhang (10.1016/j.physa.2016.07.025_br000080) 2015; 26 Gong (10.1016/j.physa.2016.07.025_br000100) 2014; 403 Fortunato (10.1016/j.physa.2016.07.025_br000090) 2010; 486 Kriegel (10.1016/j.physa.2016.07.025_br000110) 2011; 1 De Meo (10.1016/j.physa.2016.07.025_br000155) 2014; 80 Ahn (10.1016/j.physa.2016.07.025_br000175) 2010; 466 Wang (10.1016/j.physa.2016.07.025_br000040) 2013; 392 Zhang (10.1016/j.physa.2016.07.025_br000075) 2013; 87 Lancichinetti (10.1016/j.physa.2016.07.025_br000210) 2008; 78 Caldarelli (10.1016/j.physa.2016.07.025_br000145) 2002; 89 Jin (10.1016/j.physa.2016.07.025_br000045) 2013; 392 Boguñá (10.1016/j.physa.2016.07.025_br000130) 2009; 1 Ronhovde (10.1016/j.physa.2016.07.025_br000185) 2015; 2015 Huang (10.1016/j.physa.2016.07.025_br000050) 2013; 25 10.1016/j.physa.2016.07.025_br000195 Jiang (10.1016/j.physa.2016.07.025_br000055) 2013; 392 Serrano (10.1016/j.physa.2016.07.025_br000140) 2003; 68 Strehl (10.1016/j.physa.2016.07.025_br000200) 2002; 3 Knuth (10.1016/j.physa.2016.07.025_br000215) 1993 Watts (10.1016/j.physa.2016.07.025_br000010) 1998; 393 Söderberg (10.1016/j.physa.2016.07.025_br000150) 2002; 66 Rodriguez (10.1016/j.physa.2016.07.025_br000115) 2014; 344 Girvan (10.1016/j.physa.2016.07.025_br000205) 2002; 99 10.1016/j.physa.2016.07.025_br000190 Rosvall (10.1016/j.physa.2016.07.025_br000035) 2008; 105 Zhang (10.1016/j.physa.2016.07.025_br000095) 2007; 374 Barabsi (10.1016/j.physa.2016.07.025_br000005) 2012; 8 Lu (10.1016/j.physa.2016.07.025_br000170) 2011; 390 Serrano (10.1016/j.physa.2016.07.025_br000135) 2008; 100 |
| References_xml | – volume: 26 start-page: 1550096 year: 2015 ident: br000080 article-title: Community detection in bipartite networks using weighted symmetric binary matrix factorization publication-title: Internat. J. Modern Phys. C – reference: Finding Clusters of Different Sizes, Shapes, and Densities in Noisy High Dimensional Data, 2003. – volume: 3 start-page: 583 year: 2002 end-page: 617 ident: br000200 article-title: Cluster ensemblesa knowledge reuse framework for combining multiple partitions publication-title: J. Mach. Learn. Res. – volume: 390 start-page: 1150 year: 2011 end-page: 1170 ident: br000170 article-title: Link prediction in complex networks: A survey publication-title: Physica A – volume: 392 start-page: 6578 year: 2013 end-page: 6586 ident: br000160 article-title: Fuzzy overlapping community detection based on local random walk and multidimensional scaling publication-title: Physica A – volume: 99 start-page: 7821 year: 2002 end-page: 7826 ident: br000205 article-title: Community structure in social and biological networks publication-title: Proc. Natl. Acad. Sci. USA – volume: 486 start-page: 75 year: 2010 end-page: 174 ident: br000090 article-title: Community detection in graphs publication-title: Phys. Rep. – volume: 100 year: 2008 ident: br000135 article-title: Self-similarity of complex networks and hidden metric spaces publication-title: Phys. Rev. Lett. – volume: 89 year: 2002 ident: br000145 article-title: Scale-free networks from varying vertex intrinsic fitness publication-title: Phys. Rev. Lett. – volume: 87 year: 2013 ident: br000075 article-title: Overlapping community detection in complex networks using symmetric binary matrix factorization publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. – year: 1993 ident: br000215 article-title: The Stanford GraphBase: A Platform for Combinatorial Computing, Vol. 37 – volume: 54 start-page: 396 year: 2003 end-page: 405 ident: br000220 article-title: The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations publication-title: Behav. Ecol. Sociobiol. – volume: 105 start-page: 1118 year: 2008 end-page: 1123 ident: br000035 article-title: Maps of random walks on complex networks reveal community structure publication-title: Proc. Natl. Acad. Sci. USA – volume: 344 start-page: 1492 year: 2014 end-page: 1496 ident: br000115 article-title: Clustering by fast search and find of density peaks publication-title: Science – volume: 66 year: 2002 ident: br000150 article-title: General formalism for inhomogeneous random graphs publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. – reference: Clustering sentences with density peaks for multi-document summarization., in: Rada Mihalcea; Joyce Yue Chai & Anoop Sarkar, ed., ’HLT-NAACL’, The Association for Computational Linguistics, pp. 1262–1267. – volume: 70 year: 2004 ident: br000025 article-title: Finding community structure in very large networks publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. – volume: 374 start-page: 483 year: 2007 end-page: 490 ident: br000095 article-title: Identification of overlapping community structure in complex networks using fuzzy c-means clustering publication-title: Physica A – volume: 403 start-page: 71 year: 2014 end-page: 84 ident: br000100 article-title: Novel heuristic density-based method for community detection in networks publication-title: Physica A – volume: 1 start-page: 231 year: 2011 end-page: 240 ident: br000110 article-title: Density-based clustering publication-title: Data Min. Knowl. Discov. – volume: 68 year: 2003 ident: br000140 article-title: Topology of the world trade web publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. – volume: 80 start-page: 72 year: 2014 end-page: 87 ident: br000155 article-title: Mixing local and global information for community detection in large networks publication-title: J. Comput. System Sci. – volume: 22 start-page: 1547 year: 2008 end-page: 1566 ident: br000060 article-title: Identification of community structure in complex networks using affinity propagation clustering method publication-title: Modern Phys. Lett. B – volume: 56 start-page: 1 year: 2013 end-page: 12 ident: br000070 article-title: Community structure detection in social networks based on dictionary learning publication-title: Sci. China Inform. Sci. – volume: 393 start-page: 440 year: 1998 end-page: 442 ident: br000010 article-title: Collective dynamics of small-world networks publication-title: Nature – volume: 23 start-page: 425 year: 2012 end-page: 442 ident: br000065 article-title: Combinatorial model and algorithm for globally searching community structure in complex networks publication-title: J. Comb. Optim. – volume: 8 start-page: 14 year: 2012 end-page: 16 ident: br000005 article-title: The network takeover publication-title: Nat. Phys. – reference: M. Ester, H.P. Kriegel, J. Sander, et al. A density-based algorithm for discovering clusters in large spatial databases with noise, in: Published in Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, KDD-96. – volume: 81 year: 2010 ident: br000180 article-title: Local resolution-limit-free Potts model for community detection publication-title: Phys. Rev. E – volume: 389 start-page: 187 year: 2010 end-page: 197 ident: br000085 article-title: Semi-supervised clustering algorithm for community structure detection in complex networks publication-title: Physica A – volume: 78 year: 2008 ident: br000210 article-title: Benchmark graphs for testing community detection algorithms publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. – volume: 392 start-page: 4606 year: 2013 end-page: 4618 ident: br000045 article-title: Community detection in complex networks by density-based clustering publication-title: Physica A – volume: 392 start-page: 6578 year: 2013 end-page: 6586 ident: br000040 article-title: Fuzzy overlapping community detection based on local random walk and multidimensional scaling publication-title: Physica A – reference: X. Xu, N. Yuruk, Z.T. Feng, et al. Scan: a structural clustering algorithm for networks, in: Proceedings of the 13th ACM SIGKDD International, 2007, pp. 824–833. – volume: 290 start-page: 2319 year: 2000 end-page: 2323 ident: br000165 article-title: A global geometric framework for nonlinear dimensionality reduction publication-title: Science – volume: 286 start-page: 509 year: 1999 end-page: 512 ident: br000015 article-title: Emergence of scaling in random networks publication-title: Science – volume: 101 start-page: 2658 year: 2004 end-page: 2663 ident: br000020 article-title: Defining and identifying communities in networks publication-title: Proc. Natl. Acad. Sci. USA – volume: 25 start-page: 1876 year: 2013 end-page: 1889 ident: br000050 article-title: Revealing density-based clustering structure from the core-connected tree of a network publication-title: IEEE Trans. Knowl. Data Eng. – volume: 358 start-page: 593 year: 2005 end-page: 604 ident: br000030 article-title: Detection of community structures in networks via global optimization publication-title: Physica A – volume: 2015 start-page: P01001 year: 2015 ident: br000185 article-title: Local multiresolution order in community detection publication-title: J. Stat. Mech. Theory Exp. – volume: 6 start-page: 565 year: 2003 end-page: 573 ident: br000225 article-title: Community structure in Jazz publication-title: Adv. Complex Syst. – volume: 1 start-page: 74 year: 2009 end-page: 80 ident: br000130 article-title: Navigability ofcomplex networks publication-title: Nat. Phys. – reference: V. De silva, J. Tenenbaum, Global versus local methods in nonlinear dimensionality reduction, in: Proceedings of the Conference on Advances in Neural Information Processing Systems, NIPS, 2003. – volume: 392 start-page: 2182 year: 2013 end-page: 2194 ident: br000055 article-title: An efficient community detection method based on rank centrality publication-title: Physica A – volume: 466 start-page: 761 year: 2010 end-page: 764 ident: br000175 article-title: Link communities reveal multiscale complexity in networks publication-title: Nature – volume: 8 start-page: 14 issue: 2188 year: 2012 ident: 10.1016/j.physa.2016.07.025_br000005 article-title: The network takeover publication-title: Nat. Phys. doi: 10.1038/nphys2188 – ident: 10.1016/j.physa.2016.07.025_br000105 doi: 10.1145/1281192.1281280 – volume: 105 start-page: 1118 issue: 4 year: 2008 ident: 10.1016/j.physa.2016.07.025_br000035 article-title: Maps of random walks on complex networks reveal community structure publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0706851105 – volume: 78 issue: 3 year: 2008 ident: 10.1016/j.physa.2016.07.025_br000210 article-title: Benchmark graphs for testing community detection algorithms publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. – volume: 23 start-page: 425 issue: 4 year: 2012 ident: 10.1016/j.physa.2016.07.025_br000065 article-title: Combinatorial model and algorithm for globally searching community structure in complex networks publication-title: J. Comb. Optim. doi: 10.1007/s10878-010-9356-0 – volume: 392 start-page: 6578 issue: 24 year: 2013 ident: 10.1016/j.physa.2016.07.025_br000040 article-title: Fuzzy overlapping community detection based on local random walk and multidimensional scaling publication-title: Physica A doi: 10.1016/j.physa.2013.08.028 – volume: 392 start-page: 4606 issue: 19 year: 2013 ident: 10.1016/j.physa.2016.07.025_br000045 article-title: Community detection in complex networks by density-based clustering publication-title: Physica A doi: 10.1016/j.physa.2013.05.039 – volume: 54 start-page: 396 issue: 4 year: 2003 ident: 10.1016/j.physa.2016.07.025_br000220 article-title: The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations publication-title: Behav. Ecol. Sociobiol. doi: 10.1007/s00265-003-0651-y – volume: 466 start-page: 761 issue: 7307 year: 2010 ident: 10.1016/j.physa.2016.07.025_br000175 article-title: Link communities reveal multiscale complexity in networks publication-title: Nature doi: 10.1038/nature09182 – volume: 393 start-page: 440 issue: 6684 year: 1998 ident: 10.1016/j.physa.2016.07.025_br000010 article-title: Collective dynamics of small-world networks publication-title: Nature doi: 10.1038/30918 – volume: 66 issue: 6 year: 2002 ident: 10.1016/j.physa.2016.07.025_br000150 article-title: General formalism for inhomogeneous random graphs publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. doi: 10.1103/PhysRevE.66.066121 – volume: 87 issue: 6 year: 2013 ident: 10.1016/j.physa.2016.07.025_br000075 article-title: Overlapping community detection in complex networks using symmetric binary matrix factorization publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. doi: 10.1103/PhysRevE.87.062803 – volume: 486 start-page: 75 issue: 3–5 year: 2010 ident: 10.1016/j.physa.2016.07.025_br000090 article-title: Community detection in graphs publication-title: Phys. Rep. doi: 10.1016/j.physrep.2009.11.002 – volume: 99 start-page: 7821 issue: 12 year: 2002 ident: 10.1016/j.physa.2016.07.025_br000205 article-title: Community structure in social and biological networks publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.122653799 – volume: 80 start-page: 72 issue: 1 year: 2014 ident: 10.1016/j.physa.2016.07.025_br000155 article-title: Mixing local and global information for community detection in large networks publication-title: J. Comput. System Sci. doi: 10.1016/j.jcss.2013.03.012 – volume: 358 start-page: 593 issue: 2 year: 2005 ident: 10.1016/j.physa.2016.07.025_br000030 article-title: Detection of community structures in networks via global optimization publication-title: Physica A doi: 10.1016/j.physa.2005.04.022 – ident: 10.1016/j.physa.2016.07.025_br000125 – volume: 3 start-page: 583 year: 2002 ident: 10.1016/j.physa.2016.07.025_br000200 article-title: Cluster ensemblesa knowledge reuse framework for combining multiple partitions publication-title: J. Mach. Learn. Res. – year: 1993 ident: 10.1016/j.physa.2016.07.025_br000215 – volume: 286 start-page: 509 issue: 5439 year: 1999 ident: 10.1016/j.physa.2016.07.025_br000015 article-title: Emergence of scaling in random networks publication-title: Science doi: 10.1126/science.286.5439.509 – volume: 389 start-page: 187 issue: 1 year: 2010 ident: 10.1016/j.physa.2016.07.025_br000085 article-title: Semi-supervised clustering algorithm for community structure detection in complex networks publication-title: Physica A doi: 10.1016/j.physa.2009.09.018 – ident: 10.1016/j.physa.2016.07.025_br000190 – volume: 56 start-page: 1 issue: 7 year: 2013 ident: 10.1016/j.physa.2016.07.025_br000070 article-title: Community structure detection in social networks based on dictionary learning publication-title: Sci. China Inform. Sci. – volume: 26 start-page: 1550096 issue: 09 year: 2015 ident: 10.1016/j.physa.2016.07.025_br000080 article-title: Community detection in bipartite networks using weighted symmetric binary matrix factorization publication-title: Internat. J. Modern Phys. C doi: 10.1142/S0129183115500965 – volume: 1 start-page: 74 issue: 5 year: 2009 ident: 10.1016/j.physa.2016.07.025_br000130 article-title: Navigability ofcomplex networks publication-title: Nat. Phys. doi: 10.1038/nphys1130 – volume: 101 start-page: 2658 issue: 9 year: 2004 ident: 10.1016/j.physa.2016.07.025_br000020 article-title: Defining and identifying communities in networks publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0400054101 – volume: 392 start-page: 6578 issue: 24 year: 2013 ident: 10.1016/j.physa.2016.07.025_br000160 article-title: Fuzzy overlapping community detection based on local random walk and multidimensional scaling publication-title: Physica A doi: 10.1016/j.physa.2013.08.028 – volume: 374 start-page: 483 issue: 1 year: 2007 ident: 10.1016/j.physa.2016.07.025_br000095 article-title: Identification of overlapping community structure in complex networks using fuzzy c-means clustering publication-title: Physica A doi: 10.1016/j.physa.2006.07.023 – volume: 22 start-page: 1547 issue: 16 year: 2008 ident: 10.1016/j.physa.2016.07.025_br000060 article-title: Identification of community structure in complex networks using affinity propagation clustering method publication-title: Modern Phys. Lett. B doi: 10.1142/S0217984908016285 – volume: 390 start-page: 1150 issue: 6 year: 2011 ident: 10.1016/j.physa.2016.07.025_br000170 article-title: Link prediction in complex networks: A survey publication-title: Physica A doi: 10.1016/j.physa.2010.11.027 – volume: 81 issue: 4 year: 2010 ident: 10.1016/j.physa.2016.07.025_br000180 article-title: Local resolution-limit-free Potts model for community detection publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.81.046114 – volume: 100 issue: 7 year: 2008 ident: 10.1016/j.physa.2016.07.025_br000135 article-title: Self-similarity of complex networks and hidden metric spaces publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.100.078701 – volume: 6 start-page: 565 issue: 4 year: 2003 ident: 10.1016/j.physa.2016.07.025_br000225 article-title: Community structure in Jazz publication-title: Adv. Complex Syst. doi: 10.1142/S0219525903001067 – volume: 2015 start-page: P01001 issue: 1 year: 2015 ident: 10.1016/j.physa.2016.07.025_br000185 article-title: Local multiresolution order in community detection publication-title: J. Stat. Mech. Theory Exp. doi: 10.1088/1742-5468/2015/01/P01001 – volume: 25 start-page: 1876 issue: 8 year: 2013 ident: 10.1016/j.physa.2016.07.025_br000050 article-title: Revealing density-based clustering structure from the core-connected tree of a network publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2012.100 – volume: 68 issue: 6 year: 2003 ident: 10.1016/j.physa.2016.07.025_br000140 article-title: Topology of the world trade web publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. – volume: 290 start-page: 2319 issue: 5500 year: 2000 ident: 10.1016/j.physa.2016.07.025_br000165 article-title: A global geometric framework for nonlinear dimensionality reduction publication-title: Science doi: 10.1126/science.290.5500.2319 – ident: 10.1016/j.physa.2016.07.025_br000195 – volume: 1 start-page: 231 issue: 3 year: 2011 ident: 10.1016/j.physa.2016.07.025_br000110 article-title: Density-based clustering publication-title: Data Min. Knowl. Discov. doi: 10.1002/widm.30 – ident: 10.1016/j.physa.2016.07.025_br000120 – volume: 392 start-page: 2182 issue: 9 year: 2013 ident: 10.1016/j.physa.2016.07.025_br000055 article-title: An efficient community detection method based on rank centrality publication-title: Physica A doi: 10.1016/j.physa.2012.12.013 – volume: 70 issue: 6 year: 2004 ident: 10.1016/j.physa.2016.07.025_br000025 article-title: Finding community structure in very large networks publication-title: Phys. Rev. E, Stat. Nonlin. Soft Matter. Phys. doi: 10.1103/PhysRevE.70.066111 – volume: 403 start-page: 71 issue: 25 year: 2014 ident: 10.1016/j.physa.2016.07.025_br000100 article-title: Novel heuristic density-based method for community detection in networks publication-title: Physica A doi: 10.1016/j.physa.2014.01.043 – volume: 344 start-page: 1492 issue: 6191 year: 2014 ident: 10.1016/j.physa.2016.07.025_br000115 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – volume: 89 issue: 7 year: 2002 ident: 10.1016/j.physa.2016.07.025_br000145 article-title: Scale-free networks from varying vertex intrinsic fitness publication-title: Phys. Rev. Lett. |
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