Community detection via heterogeneous interaction analysis

Data Mining and Knowledge Discovery

Volumn 25, Issue 1, 2012, Pages 1-33

  Tang, Lei ^a   Wang, Xufei ^b   Liu, Huan ^b  

^a YAHOO INC   (United States)

^b Arizona State University   (United States)

Author keywords

Community detection; Heterogeneous interactions; Multi dimensional networks; Network integration; Social media

Indexed keywords

BASELINE METHODS; COMMUNITY DETECTION; COMMUNITY STRUCTURES; COMPUTATIONAL TIME; GROUND TRUTH; GROUP MEMBERS; GROUP STRUCTURE; HETEROGENEOUS INTERACTIONS; INTEGRATION SCHEME; INTEGRATION STRATEGY; MULTIPLE DIMENSIONS; NETWORK INTEGRATION; NORMALIZATION EFFECT; REAL WORLD DATA; SIMILAR INTERESTS; SOCIAL MEDIA; SOCIAL NETWORKING SITES; STRUCTURAL FEATURE; SYNTHETIC DATA; WEB 2.0; YOUTUBE;

INTEGRATION; WEBSITES;

POPULATION DYNAMICS;

EID: 84859527663     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-011-0231-0     Document Type: Article

References (62)

1. Abou-Rjeili A, Karypis G (2006) Multilevel algorithms for partitioning power-law graphs. In: Proceedings of the 20th international conference on parallel and distributed processing (IPDPS'06). IEEE Computer Society, Washington, DC, p 124
2. EM Airodi D Blei SE Fienberg EP Xing 2008 Mixed membership stochastic blockmodels J Mach Learn Res 9 1981 2014
3. Aleman-Meza B, Nagarajan M, Ramakrishnan C, Ding L, Kolari P, Sheth AP, Arpinar BI, Joshi A, Finin T (2006) Semantic analytics on social networks: experiences in addressing the problem of conflict of interest detection. In: WWW '06: proceedings of the 15th international conference on World Wide Web. ACM Press, New York, pp 407-416 (Pubitemid 46946633)
4. A Argyriou M Herbster M Pontil 2006 Combining graph Laplacians for semi-supervised learning Adv Neural Inf Process Syst 18 67
5. Asur S, Parthasarathy S, Ucar D (2007) An event-based framework for characterizing the evolutionary behavior of interaction graphs. In: KDD '07: proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 913-921 (Pubitemid 350229276)
6. Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: membership, growth, and evolution. In: KDD '06: proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 44-54 (Pubitemid 44535502)
7. Bansal N, Chiang F, Koudas N, Tompa FW (2007) Seeking stable clusters in the blogosphere. In: VLDB '07: proceedings of the 33rd international conference on very large data bases. Vienna, Austria, pp 806-817
8. Bickel S, Scheffere T (2004) Multi-view clustering. In: ICDM '04: proceedings of the fourth IEEE international conference on data mining. IEEE Computer Society, Washington, DC, pp 19-26 (Pubitemid 40731009)
9. Borg I, Groenen P (2005) Modern multidimensional scaling: theory and applications. Springer, New York
10. Chaudhuri K, Kakade SM, Livescu K, Sridharan K (2009) Multi-view clustering via canonical correlation analysis. In: ICML '09: proceedings of the 26th annual international conference on machine learning. ACM, New York, pp 1-8
11. Clauset A, Newman M, Moore C (2004) Finding community structure in very large networks. Arxiv preprint cond-mat/0408187
12. Clauset A, Shalizi CR, Newman MEJ (2007) Power-law distributions in empirical data. arXiv, 706
13. de Sa VR (2005) Spectral clustering with two views. In: Proceedings of workshop of learning with multiple views. Bonn, Germany, pp 20-27
14. Demmel J, Dongarra J, Ruhe A, van der Vorst H (2000) Templates for the solution of algebraic eigenvalue problems: a practical guide. Society for Industrial and Applied Mathematics, Philadelphia
15. Fern XZ, Brodley CE (2004) Solving cluster ensemble problems by bipartite graph partitioning. In: ICML '04: proceedings of the twenty-first international conference on machine learning. ACM, New York, p 36
16. S Fortunato 2010 Community detection in graphs Phys Rep 486 3-5 75 174 2580414 10.1016/j.physrep.2009.11.002
17. S Fortunato M Barthelemy 2007 Resolution limit in community detection Proc Natl Acad Sci USA 104 1 36 41 10.1073/pnas.0605965104 (Pubitemid 46075032)
18. Goder A, Filkov V (2008) Consensus clustering algorithms: comparison and refinement. In: Proceedings of the workshop on algorithm engineering and experiments, ALENEX 2008, San Francisco, CA, January 19, 2008
19. B Good Y De Montjoye A Clauset 2010 Performance of modularity maximization in practical contexts Phys Rev E 81 4 046106 2736215 10.1103/PhysRevE.81.046106
20. Guimera R, Amaral LAN (2005) Functional cartography of complex metabolic networks. Nature 433:895-900 (Pubitemid 40314902)
21. Hopcroft J, Khan O, Kulis B, Selman B (2003) Natural communities in large linked networks. In: KDD '03: proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 541-546
22. H Hotelling 1936 Relations between two sets of variates Biometrika 28 3/4 321 377 0015.40705 10.2307/2333955
23. T Hu SY Sung 2005 Consensus clustering Intell Data Anal 9 6 551 565
24. Jung J, Euzenat J (2007) Towards semantic social networks. In: Proceedings of the European semantic Web conference (ESWC2007), volume 4519 of LNCS. Springer, Berlin, pp 267-280 (Pubitemid 47290013)
25. H Kang L Getoor L Singh 2007 Visual analysis of dynamic group membership in temporal social networks SIGKDD Explor Spec Issue Vis Anal 9 2 13 21 10.1145/1345448.1345452
26. Kemp C, Tenenbaum J, Griffiths T, Yamada T, Ueda N (1999/2006) Learning systems of concepts with an infinite relational model. In: Proceedings of the national conference on artificial intelligence, vol 21. AAAI Press/MIT Press, Menlo Park/Cambridge, p 381
27. J Kettenring 1971 Canonical analysis of several sets of variables Biometrika 58 433 451 341750 0225.62072 10.1093/biomet/58.3.433
28. JM Kleinberg 1999 Authoritative sources in a hyperlinked environment J ACM 46 5 604 632 1747649 1065.68660 10.1145/324133.324140
29. A Lancichinetti S Fortunato 2009 Community detection algorithms: a comparative analysis Phys Rev E 80 5 056117 10.1103/PhysRevE.80.056117
30. Leskovec J, Lang KJ, Mahoney M (2010) Empirical comparison of algorithms for network community detection. In: WWW '10: proceedings of the 19th international conference on World Wide Web. ACM, New York, pp 631-640
31. Lin Y-R, Chi Y, Zhu S, Sundaram H, Tseng BL (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceeding of the 17th international conference on World Wide Web (WWW '08). ACM, New York, NY, pp 685-694
32. Y-R Lin Y Chi S Zhu H Sundaram BL Tseng 2009 Analyzing communities and their evolutions in dynamic social networks ACM Trans Knowl Discov Data 3 2 1 31 10.1145/1514888.1514891
33. Lizorkin D, Medelyan O, Grineva M (2009) Analysis of community structure in Wikipedia. In: WWW '09: proceedings of the 18th international conference on World Wide Web. ACM, New York, pp 1221-1222
34. Long B, Yu PS, Zhang ZM (2008) A general model for multiple view unsupervised learning. In: Proceedings of the 2008 SIAM international conference on data mining, pp 822-833
35. Uv Luxburg 2007 A tutorial on spectral clustering Stat Comput 17 4 395 416 2409803 10.1007/s11222-007-9033-z
36. M McPherson L Smith-Lovin JM Cook 2001 Birds of a feather: homophily in social networks Annu Rev Sociol 27 415 444 10.1146/annurev.soc.27.1.415 (Pubitemid 33342718)
37. Mika P (2007) Ontologies are us: a unified model of social networks and semantics. In: Web semantics: science, services and agents on the World Wide Web, vol 5, no 1, pp 5-15. Selected papers from the international semantic Web conference (ISWC2005)
38. K Miller T Griffiths M Jordan 2009 Nonparametric latent feature models for link prediction Adv Neural Inf Process Syst 22 1276 1284
39. S Monti P Tamayo J Mesirov T Golub 2003 Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data Mach Learn 52 1-2 91 118 1039.68103 10.1023/A:1023949509487
40. P Mucha T Richardson K Macon M Porter J Onnela 2010 Community structure in time-dependent, multiscale, and multiplex networks Science 328 5980 876 878 2662590 1226.91056 10.1126/science.1184819
41. M Newman 2006 Finding community structure in networks using the eigenvectors of matrices Phys Rev E 74 3 036104 2282139 10.1103/PhysRevE.74. 036104 (Pubitemid 44521558)
42. M Newman 2006 Modularity and community structure in networks Proc Natl Acad Sci USA 103 23 8577 8582 10.1073/pnas.0601602103 (Pubitemid 43878062)
43. Nguyen N, Caruana R (2007) Consensus clusterings. In: ICDM '07: proceedings of the 2007 seventh IEEE international conference on data mining. IEEE Computer Society, Washington, DC, pp 607-612
44. G Palla A-L Barabasi T Vicsek 2007 Quantifying social group evolution Nature 446 7136 664 667 10.1038/nature05670 (Pubitemid 46606882)
45. Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: KDD '02: proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 61-70
46. M Rosvall C Bergstrom 2008 Maps of random walks on complex networks reveal community structure Proc Natl Acad Sci USA 105 4 1118 1123 10.1073/pnas.0706851105 (Pubitemid 351397014)
47. P Sarkar AW Moore 2005 Dynamic social network analysis using latent space models SIGKDD Explor Newsl 7 2 31 40 10.1145/1117454.1117459
48. Specia L, Motta E (2007) Integrating folksonomies with the semantic web. In: Proceedings of the 4th European conference on the semantic Web: research and applications. Springer, Berlin, pp 624-639 (Pubitemid 47290036)
49. A Strehl J Ghosh 2003 Cluster ensembles-a knowledge reuse framework for combining multiple partitions J Mach Learn Res 3 583 617 1991087 1084.68759
50. Tang L, Liu H (2009a) Relational learning via latent social dimensions. In: KDD '09: proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 817-826
51. Tang L, Liu H (2009b) Scalable learning of collective behavior based on sparse social dimensions. In: CIKM '09: proceeding of the 18th ACM conference on information and knowledge management. ACM, New York, pp 1107-1116
52. Tang L, Liu H, Zhang J, Nazeri Z (2008) Community evolution in dynamic multi-mode networks. In: KDD '08: proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 677-685
53. Tang L, Wang X, Liu H (2009a) Uncovering groups via heterogeneous interaction analysis. In: Proceeding of IEEE international conference on data mining. IEEE Computer Society, Washington, DC, pp 503-512
54. Tang W, Lu Z, Dhillon IS (2009b) Clustering with multiple graphs. In: ICDM '09: proceedings of the 2009 ninth IEEE international conference on data mining. IEEE Computer Society, Washington, DC, pp 1016-1021
55. Topchy A, Jain AK, Punch W (2003) Combining multiple weak clusterings. In: ICDM '03: proceedings of the third IEEE international conference on data mining. IEEE Computer Society, Washington, DC, p 331
56. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge
57. White S, Smyth P (2005) A spectral clustering approach to finding communities in graphs. In: Proceedings of the fifth SIAM international conference on data mining, pp 274-285
58. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufman, San Francisco
59. Yingzi Jin YM, Ishizuka M (2008) Ranking entities on the web using social network mining and ranking learning. In: WWW '08: proceedings of the 17th international conference on World Wide Web. ACM Press, New York, pp 21-25
60. Yu K, Yu S, Tresp V (2005) Soft clustering on graphs. In: Advances in neural information processing systems 18. MIT Press, Cambridge, pp 1553-1560
61. Zhou D, Burges CJC (2007) Spectral clustering and transductive learning with multiple views. In: ICML '07: proceedings of the 24th international conference on machine learning. ACM, New York, 1159-1166
62. Zhou D, Zhu S, Yu K, Song X, Tseng BL, Zha H, Giles CL (2008) Learning multiple graphs for document recommendations. In: WWW '08: proceeding of the 17th international conference on World Wide Web. ACM, New York, pp 141-150