Towards community detection in locally heterogeneous networks

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 391-402

  Aggarwal, Charu C ^a   Xie, Yan ^b   Yu, Philip S ^b  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

Clustering; Social networks

Indexed keywords

DATA MINING; GRAPH THEORY; HETEROGENEOUS NETWORKS; SOCIAL NETWORKING (ONLINE);

CLUSTERING; COMMUNITY DETECTION; GLOBAL COMMUNITY; GRAPH STRUCTURES; LOCAL HETEROGENEITY; SOCIAL COMMUNITIES; STRUCTURAL INFORMATION; THEORETICAL ASPECTS;

SPACE DIVISION MULTIPLE ACCESS;

EID: 84860850614     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.34     Document Type: Conference Paper

References (23)

1. J. Abello, M. G. Resende, and S. Sudarsky, Massive quasi-clique detection, LATIN, (2002), pp. 598-612.
2. L. Backstrom, D. P. Huttenlocher, J. M. Kleinberg, and X. Lan, Group formation in large social networks: membership, growth, and evolution, KDD Conf., (2006), pp. 44-54.
3. C. Aggarwal and H. Wang, Managing and Mining Graph Data, Springer, (2010).
4. C. Aggarwal, Social Network Data Analytics, Springer, (2011).
5. D. Bortner and J. Han, Progressive clustering of networks using structure-connected order of traversal, ICDE Conf., (2010), pp. 653-656.
6. D. Chakrabarti, R. Kumar, and A. Tomkins, Evolutionary clustering, KDD Conf., (2006), pp. 554-560.
7. Y. Chi, X. Song, D. Zhou, K. Hino, and B. L. Tseng, Evolutionary spectral clustering by incorporating temporal smoothness, KDD Conf., (2007), pp. 153-162.
8. A. Clauset, M. E. J. Newman, and C. Moore, Finding community structure in very large networks, Phys. Rev. E 70, 066111, (2004).
9. E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, and J. Ullman, and C. Yang, Finding interesting associations without support pruning, IEEE TKDE, 13(1), (2001), pp. 64-78.
10. M. Faloutsos, P. Faloutsos, and C. Faloutsos, On power law relationships of the internet topology, SIGCOMM, (1999), pp. 251-262.
11. D. Gibson, R. Kumar, and A. Tomkins, Discovering large dense subgraphs in massive graphs, VLDB Conf., (2005), pp. 721-732.
12. M.-S. Kim and J. Han, A particle-and-density based evolutionary clustering method for dynamic networks, PVLDB, 2(1), (2009), pp. 622-633.
13. R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, Trawling the web for emerging cyber-communities, Computer Networks 31(11-16) (1999), pp. 1481-1493.
14. J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney, Statistical properties of community structure in large social and information networks, WWW Conf., (2008), pp. 695-704.
15. Y.-R. Lin, J. Sun, P. Castro, R. B. Konuru, H. Sundaram, and A. Kelliher, Extracting community structure through relational hypergraphs, WWW Conf, (2009), pp. 1213-1214.
16. B. Liu, W. Hsu, and Y. Ma, Mining association rules with multiple minimum supports, KDD Conf., (1999), pp. 337-341.
17. J. Pei, D. Jiang, and A. Zhang, On mining cross-graph quasi-cliques, KDD Conf., (2005), pp. 228-238.
18. M. Rattigan, M. Maier, and D. Jensen, Graph Clustering with Network Structure Indices, ICML Conf, (2007), pp. 783-790.
19. V. Satuluri, and S. Parthasarathy, Scalable graph clustering using stochastic flows: applications to community discovery, KDD Conf. (2009), pp. 737-746.
20. L. Tang and H. Liu, Graph mining applications to social network analysis, Managing and Mining Graph Data, Ed. Charu Aggarwal, Haixun Wang, (2010).
21. T. Yang, R. Jin, Y. Chi, and S. Zhu, Combining link and content for community detection: a discriminative approach, KDD Conf, (2009), pp. 927-936.
22. Z. Zeng, J. Wang, L. Zhou, and G. Karypis, Out-of-core coherent closed quasi-clique mining from large dense graph databases, ACM Transactions on Database Systems, Vol 31(2), (2007).
23. Y. Zhou, H. Cheng, and J. X. Yu, Graph clustering based on structural/attribute similarities, Proc. VLDB Endow., 2(1), (2009), pp. 718-729.