Local graph sparsification for scalable clustering

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2011, Pages 721-732

  Satuluri, Venu ^a   Parthasarathy, Srinivasan ^a   Ruan, Yiye ^a  

^a Ohio State University   (United States)

Author keywords

graph clustering; graph sparsification; minwise hashing

Indexed keywords

GRAPH THEORY;

COMMUNITY DISCOVERIES; DISCOVERY ALGORITHM; EDGE-SETS; GRAPH CLUSTERING; GRAPH SPARSIFICATION; MINWISE HASHING; REAL NETWORKS; SCALABLE CLUSTERING; SIMPLE++; STATE OF THE ART;

CLUSTERING ALGORITHMS;

EID: 79959955223     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1989323.1989399     Document Type: Conference Paper

References (38)

1. D. Achlioptas and F. McSherry. Fast computation of low rank matrix approximations. In STOC '01, pages 611-618, 2001.
2. S. Arora, E. Hazan, and S. Kale. A fast random sampling algorithm for sparsifying matrices. APPROX-RANDOM '06, pages 272-279, 2006.
3. L. Becchetti, P. Boldi, C. Castillo, and A. Gionis. Efficient semi-streaming algorithms for local triangle counting in massive graphs. In KDD '08, pages 16-24, 2008.
4. A. A. Benczúr and D. R. Karger. Approximating s-t minimum cuts in O(n2) time. In STOC '96, pages 47-55, 1996.
5. T. Bohman, C. Coopery, and A. Friezez. Min-Wise independent linear permutations. the electronic journal of combinatorics, 7(R26):2, 2000.
6. A. Z. Broder, M. Charikar, A. M. Frieze, and M. Mitzenmacher. Min-wise independent permutations (extended abstract). In STOC '98, pages 327-336, 1998.
7. G. Buehrer and K. Chellapilla. A scalable pattern mining approach to web graph compression with communities. In WSDM '08, pages 95-106, 2008.
8. A. Clauset, C. Shalizi, and M. Newman. Power-law distributions in empirical data. SIAM review, 51(4):661-703, 2009.
9. M. Costanzo, A. Baryshnikova, J. Bellay, Y. Kim, E. Spear, C. Sevier, H. Ding, J. Koh, K. Toufighi, S. Mostafavi, et al. The genetic landscape of a cell. Science's STKE, 327(5964):425, 2010.
10. B. B. Dalvi, M. Kshirsagar, and S. Sudarshan. Keyword search on external memory data graphs. PVLDB, 1(1):1189-1204, 2008.
11. I. S. Dhillon, Y. Guan, and B. Kulis. Weighted Graph Cuts without Eigenvectors: A Multilevel Approach. IEEE Trans. PAMI, 29(11):1944-1957, 2007.
12. S. Fortunato. Community detection in graphs. Phys. Reports, 486:75-174, 2010.
13. D. Gibson, R. Kumar, and A. Tomkins. Discovering large dense subgraphs in massive graphs. In VLDB '05, pages 721-732, 2005.
14. J. Glattfelder and S. Battiston. Backbone of complex networks of corporations: The flow of control. Phys. Rev. E, 80(3):36104, 2009.
15. R. C. K. and S. Sudarshan. Graph clustering for keyword search. In COMAD '09, 2009.
16. D. R. Karger. Random sampling in cut, flow, and network design problems. In STOC '94, pages 648-657, 1994.
17. G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 20(1):359, 1999.
18. V. Krishnamurthy, M. Faloutsos, M. Chrobak, L. Lao, J. Cui, and A. Percus. Reducing large internet topologies for faster simulations. NETWORKING '05, pages 328-341, 2005.
19. H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In WWW '10, pages 591-600, 2010.
20. A. Lancichinetti, S. Fortunato, and F. Radicchi. Benchmark graphs for testing community detection algorithms. Phys. Rev. E, 78(4):46110, 2008.
21. K. Lang and S. Rao. A flow-based method for improving the expansion or conductance of graph cuts. LNCS '04, pages 325-337, 2004.
22. J. Leskovec and C. Faloutsos. Sampling from large graphs. In KDD '06, pages 631-636, 2006.
23. J. Leskovec, K. Lang, A. Dasgupta, and M. Mahoney. Statistical properties of community structure in large social and information networks. In WWW '08, pages 695-704, 2008.
24. S. Macskassy and F. Provost. Classification in networked data: A toolkit and a univariate case study. The Journal of Machine Learning Research, 8:935-983, 2007. (Pubitemid 46798412)
25. A. S. Maiya and T. Y. Berger-Wolf. Sampling community structure. In WWW '10, pages 701-710, 2010.
26. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and Analysis of Online Social Networks. In IMC '07, pages 29-42, October 2007.
27. M. Newman and M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E, 69(2):026113, Feb 2004.
28. F. Provost and V. Kolluri. A survey of methods for scaling up inductive algorithms. DMKD, 3(2):131-169, 1999.
29. S. Pu, J. Wong, B. Turner, E. Cho, and S. Wodak. Up-to-date catalogues of yeast protein complexes. Nucleic acids research, 37(3):825-831, 2009.
30. A. Ruepp et al. CORUM: the comprehensive resource of mammalian protein complexes. Nucleic Acids Research, 36(suppl 1):D646, 2008.
31. V. Satuluri and S. Parthasarathy. Scalable graph clustering using stochastic flows: applications to community discovery. In KDD '09, pages 737-746, 2009.
32. V. Satuluri and S. Parthasarathy. Symmetrizations for clustering directed graphs. In EDBT '11, 2011.
33. V. Satuluri, S. Parthasarathy, and Y. Ruan. Local graph sparsification for scalable clustering. Technical Report OSU-CISRC-11/10-TR25, The Ohio State University.
34. J. Shi and J. Malik. Normalized Cuts and Image Segmentation. IEEE Trans. PAMI, 22(8), 2000.
35. D. A. Spielman and N. Srivastava. Graph sparsification by effective resistances. In STOC '08, pages 563-568, 2008.
36. D. A. Spielman and S. Teng. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. In STOC '04, pages 81-90, 2004.
37. M. Tumminello, T. Aste, T. Di Matteo, and R. Mantegna. A tool for filtering information in complex systems. PNAS, 102(30):10421, 2005.
38. S. Van Dongen. Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht, 2000.