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Motif-based embedding for graph clustering. (English) Zbl 1456.68154
MSC:
68T05 Learning and adaptive systems in artificial intelligence
05C82 Small world graphs, complex networks (graph-theoretic aspects)
62H30 Classification and discrimination; cluster analysis (statistical aspects)
68R10 Graph theory (including graph drawing) in computer science
Software:
DBpedia; GVF
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[1] Arenas A, Fernandez A, Fortunato S and Gomez S 2008 Motif-based communities in complex networks J. Phys. A: Math. Theor.41 224001
[2] Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R and Ives Z 2007 Dbpedia: a nucleus for a web of open data Proc. of ISWC pp 722-35
[3] Barnes J and Hut P 1986 A hierarchical o(nlogn) force-directed calculation algorithm Nature324 446-9
[4] Benson A, Gleich D and Leskovec J 2015 Tensor spectral clustering for partitioning higher-order network structures Proc. of SDM pp 118-26
[5] Benson A, Gleich D and Leskovec J 2016 Higher-order organization of complex networks Science353 163-6
[6] Blondel V D, Guillaume J-L, Lambiotte R and Lefebvre E 2010 Fast unfolding of communities in large networks J. Stat. Mech. P10008
[7] Burt R S 1980 Models of network structure Annu. Rev. Sociol.6 79-141
[8] Chen W-Y, Song Y, Bai H, Lin C-J and Chang E Y 2010 Parallel spectral clustering in distributed systems IEEE Trans. Pattern Anal. Mach. Intell.33 568-86
[9] Danon L, Diaz-Guilera A, Duch J and Arenas A 2005 Comparing community structure identification J. Stat. Mech. P09008
[10] Davidson R and Harel D 1996 Drawing graphs nicely using simulated annealing ACM Trans. Graph.15 301-31
[11] Dhillon I S 2003 Co-clustering documents and words using bipartite spectral graph partitioning Proc. of ACM SIGKDD pp 269-74
[12] Fortunato S 2010 Community detection in graphs Phys. Rep.486 75-174
[13] Fruchterman T M J and Reingold E M 1991 Graph drawing by force-directed placement Softw.: Pract. Exp.21 1129-64
[14] Greene D and Cunningham P 2013 Producing a unified graph representation from multiple social network views Proc. of WebSci pp 118-21
[15] Herman I, Melancon G and Marshall M S 2000 Graph visualization and navigation in information visualization: a survey IEEE Trans. Vis. Comput. Graphics6 24-43
[16] Kim J and Lee J-G 2015 Community detection in multi-layer graphs: a survey ACM SIGMOD Rec.44 37-48
[17] Klymko C, Gleich D F and Kolda T G 2014 Using triangles to improve community detection in directed networks Proc. of ASE BigDataScience
[18] Kolda T G, Pinar A and Seshadhri C 2013 Triadic measures on graphs: the power of wedge sampling Proc. of SDM pp 10-18
[19] Kossinets G and Watts D J 2006 Empirical analysis of an evolving social network Science311 88-90
[20] Lancichinetti A and Fortunato S 2009 Community detection algorithms: a comparative analysis Phys. Rev. E 80 056117
[21] Lancichinetti A, Fortunato S and Radicchi F 2008 Benchmark graphs for testing community detection algorithms Phys. Rev. E 78 046110
[22] Larremore D B, Clauset A and Buckee C O 2013 A network approach to analyzing highly recombinant malaria parasite genes PLoS Comput. Biol.9 e1003268
[23] Lim S, Kim J and Lee J-G 2016 BlackHole: robust community detection inspired by graph drawing Proc. of IEEE ICDE pp 25-36
[24] Lim S, Ryu S, Kwon S, Jung K and Lee J-G 2014 LinkSCAN: overlapping community detection using the link-space transformation Proc. of IEEE ICDE pp 292-303
[25] Newman M E J 2013 Spectral methods for community detection and graph partitioning Phys. Rev. E 88 042822
[26] Newman M E J, Watts D J and Strogatz S H 2002 Random graph models of social networks Proc. Natl Acad. Sci. USA99 2566-72
[27] Ng A Y, Jordan M I and Weiss Y 2001 On spectral clustering: analysis and an algorithm Proc. of NIPS pp 849-56
[28] Noack A 2007 Energy models for graph clustering J. Graph. Algebr. Appl.11 453-80
[29] Noack A 2009 Modularity clustering is force-directed layout Phys. Rev. E 79 026102
[30] Radicchi F, Castellano C, Cecconi F, Loreto V and Parisi D 2004 Defining and identifying communities in networks Proc. Natl Acad. Sci. USA101 2658-63
[31] Raghavan U N, Albert R and Kumara S 2007 Near linear time algorithm to detect community structures in large-scale networks Phys. Rev. E 76 036106
[32] Reichardt J and Leone M 2008 (Un)detectable cluster structure in sparse networks Phys. Rev. Lett.101 078701
[33] Rosvall M and Bergstrom C T 2008 Maps of random walks on complex networks reveal community structure Proc. Natl Acad. Sci. USA105 1118-23
[34] Rosvall M and Bergstrom C T 2011 Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems PLoS One6 e18209
[35] Rosvall M, Esquivel A V, Lancichinetti A, West J D and Lambiotte R 2014 Memory in network flows and its effects on spreading dynamics and community detection Nat. Commun.5 4630
[36] Ruan Y, Fuhry D and Parthasarathy S 2013 Efficient community detection in large networks using content and links Proc. of WWW pp 1089-98
[37] Schaeffer S E 2007 Graph clustering Comput. Sci. Rev.1 27-64
[38] Shi J and Malik J 2000 Normalized cuts and image segmentation IEEE Trans. Pattern Anal. Mach. Intell.22 888-905
[39] Tamassia R 2013 Handbook of Graph Drawing and Visualization (London: Chapman and Hall/CRC)
[40] von Luxburg U 2007 A tutorial on spectral clustering Stat. Comput.17 395-416
[41] Šubelj L and Bajec M 2011 Community structure of complex software systems: analysis and applications Physica A 390 2968-75
[42] Wang C, Wang H, Liu J, Ji M, Su L, Chen Y and Han J 2013 On the detectability of node grouping in networks Proc. of SDM pp 713-21
[43] Yang Z, Peltonen J and Kaski S 2014 Optimization equivalence of divergences improves neighbor embedding Proc. of ICML pp 460-8
[44] Zha H, He X, Ding C, Simon H and Gu M 2001 Bipartite graph partitioning and data clustering Proc. of ACM CIKM pp 25-32
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