FURS: Fast and Unique Representative Subset selection retaining large-scale community structure

Social Network Analysis and Mining

Volumn 3, Issue 4, 2013, Pages 1075-1095

  Mall, Raghvendra ^a   Langone, Rocco ^a   Suykens, Johan A K ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Community detection; Hubs; Node subset selection; Simple diffusion model

Indexed keywords

DEFORESTATION; DIFFUSION; LARGE SCALE SYSTEMS; QUALITY CONTROL; SOCIAL SCIENCES;

COMMUNITY DETECTION; DIFFUSION MODEL; EIGENVECTOR CENTRALITIES; HUBS; OUT-OF-SAMPLE EXTENSION; STATE-OF-THE-ART METHODS; SUBSET SELECTION; VARIATION OF INFORMATIONS;

SET THEORY;

EID: 84921713163     PISSN: 18695450     EISSN: 18695469     Source Type: Journal    
DOI: 10.1007/s13278-013-0144-6     Document Type: Article

References (58)

1. Adler M, Mitzenmacher M (2000) Towards compressing web graphs. In: Proceedings of IEEE DCC, pp 203–212
2. Alzate C, Suykens J (2010) Multiway spectral clustering with out-of-sample extensions through weighted PCA. IEEE Trans Pattern Anal Mach Intell 32(2):335–347
3. Blondel V, Guillaume J, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10(P10008)
4. Blume L (1993) The statistical mechanics of strategic interaction. Games Econ Behav 5(3):387–424
5. Boguna M, Pastor-Satorras R, Diaz-Guilera A, Arenas A (2004) Models of social networks based on social distance attachment. Phys Rev E 70(5)
6. Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92(5):1170–1182
7. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev Neurosci 10(4)
8. Catanese SA, De Meo P, Ferrara E, Fiumara G, Provetti A (2011) Crawling facebook for social network analysis purposes. In: Proceedings of international conference on web intelligence, mining and semantics, p 52
9. Clauset A, Newman M, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(066111)
10. Coleman J, Katz E, Menzel H (1966) Medical innovation: a diffusion study. Bobbs-Merrill, Indianapolis
11. Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. In: KDD’08, pp 160–168
12. Danon L, Diáz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 09(P09008+)
13. Duch J, Arenas A (2005) Community detection in complex networks using external optimization. Phys Rev E 72(2):027104+
14. Ellison G (1993) Learning, local interaction and coordination. Econometrica 61(5):1047–1071
15. Feder T, Motwani R (1991) Clique partitions, graph compression and speeding-up algorithms. J Comput Syst Sci 123–133
16. Feige U (1998) A threshold of ln for approximating set cover. J ACM 45(4):634–652
17. Ferrara E (2012) A large-scale community structure analysis in facebook. EPJ Data Sci 1(9):1–30
18. Fortunato S. (2009) Community detection in graphs. Phys Rep 486:75–174
19. Frank O (2005) Network sampling and model fitting. Cambridge Press University, Cambridge
20. Freeman L (1979) Centrality in social networks: conceptual clarification. Soc Netw 1(3):215–239
21. Gilbert A, Levchenko K (2004) Compressing network graphs. In: Proceedings of LinkKDD workshop at the 10th ACM Conference on KDD
22. Gilbert et al (2011) Communities and hierarchical structures in dynamic social networks: analysis and visualization. Soc Netw Anal Min 1(2):83–95
23. Girvan M, Newman M (2002) Community structure in social and biological networks. PNAS 99(12):7821–7826
24. Gjoka M, Kurant M, Butts CT, Markopoulou A (2010) Walking in facebook: a case study of unbiased sampling of OSNs. In: Proceedings of IEEE INFOCOM, pp 1–9
25. Gleich D, Seshadhri C (2012) Vertex neighborhoods, low conductance cuts, and good seeds for local community methods. In: Proc of KDD’12, pp 597–605
26. Goel S, Salganik M (2009) Respondent-driven sampling as markov chain Monte Carlo. Stat Med 17(28):2202–2229
27. Goldenberg J, Libai B, Muller E (2001) Using complex system analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata. Acad Market Sci Rev 1(9)
28. Goyal S (1996) Interaction structure and social change. J Inst Theor Econ 152:472–495
29. Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83(6):1420–1443
30. Hubert L, Arabie P (1985) Comparing partitions. J Classif 2(1):193–218
31. Hubler C, Kriegel H, Borgwardt K, Ghahramani Z. (2008) Metropolis algorithms for representative subgraph sampling. In: ICDM’08, pp 283–292
32. Jeong H, Tombor B, Albert R, Oltvai Z, Barabasi A (2000) The large-scale organization of metabolic networks. Nature 407(6804):651–654
33. Kang U, Faloutsos C (2011) Beyond ‘caveman communities’: hubs and spokes for graph compression and mining. In: Proceedings of ICDM’11, pp 300–309
34. Katz L (1953) A new status index derived from sociometric index. Psychometrika 39–43
35. Kempe D, Kleinberg J, Tardos E (2005) Influential nodes in a diffusion model for social networks. In: Proceedings of 32nd international colloquium on automata, languages and programming (ICALP)
36. Lancichinetti A, Fortunato S (2009a) Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys Rev E 80(1):116–118
37. Lancichinetti A, Fortunato S (2009b) Community detection algorithms: a comparative analysis. Phys Rev E 80(056117)
38. Lancichinetti A, Fortunato S, Kertész J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11(033015)
39. Langone R, Alzate C, Suykens J (2012) Kernel spectral clustering for community detection in complex networks. In: IEEE WCCI/IJCNN, pp 2596–2603
40. Leskovec J, Backstrom L, Kumar R, Tomkins A (2008) Microscopic evolution of social networks. In: KDD’08, pp 462–470
41. Leskovec J, Faloutsos C (2006) Sampling from large graphs. In: KDD’06, pp 631–636
42. Liggett T (1985) Interacting particle systems. Springer, Berlin
43. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, pp 281–297
44. Maiya A, Berger-Wolf T (2010) Sampling community structure. In: WWW’10, pp 631–636
45. Mall R, Langone R, Suykens J (2013) Kernel spectral clustering for big data networks. Entropy, Special Issue: Big Data, 15(5):1567–1586
46. Mehler A, Skiena S (2009) Expanding network communities from representative examples. ACM Trans Knowl Discov Data 3(2):1–27
47. Meila M (2007) Comparing clustering information based distance. J Multivar Anal 98(5):873–895
48. Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092
49. Morris S (2000) Contagion. Rev Econ Stud 67(1):57–78
50. Newman M (2006) Finding community structure in networks using eigenvectors of matrices. Phys Rev E 74(3)
51. Pham M, Klamma R, Jarke M (2011) Development of computer science disciplines: a social network analysis approach. Soc Netw Anal Min 1(4):321–340
52. Rabbany R, Takaffoli M, Fagnan J, Zaiane OR, Campello RJGB (2012) Relative validity criteria for community mining algorithms. In: International conference on advances in social networks analysis and mining (ASONAM), pp 258–265
53. Rafiei D (2005) Effectively visualizing large networks through sampling. In Proceedings of VIS 05, pp 375–382
54. Rosvall M, Bergstrom C (2008) Maps of random walks on complex networks reveal community structure. PNAS 105:1118–1123
55. Ryan B, Gross N (1943) The diffusion of hybrid seed corn in two Iowa communities. Rural Sociol 8:15–24
56. Saravanan M, Prasad GKK, Suganthi D (2011) Analyzing and labeling telecom communities using structural properties. Soc Netw Anal Min 1(4):271–286
57. Shelling T (1978) Micromotives and macrobehavior. Norton, New York
58. Wu J, Xiong H, Chen J (2009) Adapting the right measures for k-means clustering. In: Proceedings of SIGKDD’09, pp 877–886