A local algorithm for finding well-connected clusters

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 2, 2013, Pages 1433-1441

  Zhu, Zeyuan Allen ^a   Lattanzi, Silvio ^b   Mirrokni, Vahab ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

CLUSTERING ACCURACY; EMPIRICAL EVALUATIONS; GRAPH CLUSTERING; LOCAL ALGORITHM; LOCAL CLUSTERING; RUNNING TIME; SYNTHETIC AND REAL DATA; THEORETICAL GUARANTEES;

CLUSTERING ALGORITHMS;

EID: 84897539616     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Alamgir, Morteza and von Luxburg, Ulrike. Multi-agent random walks for local clustering on graphs. ICDM '10, pp. 18-27, 2010.
2. Alon, Noga. Eigenvalues and expanders. Combinatorica, 6(2):83-96, 1986.
3. Alvisi, L., Clement, A., Epasto, A., Lattanzi, S., and Panconesi, A. The evolution of sybil defense via social networks. In IEEE Symposium on Security and Privacy, 2013.
4. Andersen, Reid and Lang, Kevin J. Communities from seed sets. WWW '06, pp. 223-232, 2006.
5. Andersen, Reid and Peres, Yuval. Finding sparse cuts locally using evolving sets. STOC, 2009.
6. Andersen, Reid, Chung, Fan, and Lang, Kevin. Using pagerank to locally partition a graph. 2006. An extended abstract appeared in FOCS '2006.
7. Andersen, Reid, Gleich, David F., and Mirrokni, Vahab. Overlapping clusters for distributed computation. WSDM '12, pp. 273-282, 2012.
8. Arora, Sanjeev and Kale, Satyen. A combinatorial, primal-dual approach to semidefinite programs. STOC '07, pp. 227-236, 2007.
9. Arora, Sanjeev, Rao, Satish, and Vazirani, Umesh V. Expander flows, geometric embeddings and graph partitioning. Journal of the ACM, 56(2), 2009.
10. 2) time. SIAM Journal on Computing, 39(5):1748-1771, 2010.
11. Chawla, Shuchi, Krauthgamer, Robert, Kumar, Ravi, Rabani, Yuval, and Sivakumar, D. On the hardness of approximating multicut and sparsest-cut. Computational Complexity, 15(2):94-114, June 2006.
12. Gargi, Ullas, Lu, Wenjun, Mirrokni, Vahab S., and Yoon, Sangho. Large-scale community detection on youtube for topic discovery and exploration. In AAAI Conference on Weblogs and Social Media, 2011.
13. Gharan, Shayan Oveis and Trevisan, Luca. Approximating the expansion profile and almost optimal local graph clustering. FOCS, pp. 187-196, 2012.
14. Gleich, David F. and Seshadhri, C. Vertex neighborhoods, low conductance cuts, and good seeds for local community methods. In KDD '2012, 2012.
15. Haveliwala, Taher H. Topic-sensitive pagerank. In WWW '02, pp. 517-526, 2002.
16. Kannan, Ravi, Vempala, Santosh, and Vetta, Adrian. On clusterings: Good, bad and spectral. Journal of the ACM, 51(3):497-515, 2004.
17. Leighton, Frank Thomson and Rao, Satish. Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. Journal of the ACM, 46(6):787-832, 1999.
18. Leskovec, Jure, Lang, Kevin J., Dasgupta, Anirban, and Mahoney, Michael W. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics, 6(1):29-123, 2009.
19. Leskovec, Jure, Lang, Kevin J., and Mahoney, Michael. Empirical comparison of algorithms for network community detection. WWW, 2010.
20. Lin, Frank and Cohen, William W. Power iteration clustering. In ICML '10, pp. 655-662, 2010.
21. Lovász, László and Simonovits, Miklós. The mixing rate of markov chains, an isoperimetric inequality, and computing the volume. FOCS, 1990.
22. Lovász, László and Simonovits, Miklós. Random walks in a convex body and an improved volume algorithm. Random Struct. Algorithms, 4(4):359-412, 1993.
23. Makarychev, Konstantin, Makarychev, Yury, and Vijayaraghavan, Aravindan. Approximation algorithms for semi-random partitioning problems. In STOC '12, pp. 367-384, 2012.
24. Merca, Mircea. A note on cosine power sums. Journal of Integer Sequences, 15:12.5.3, May 2012.
25. Morris, Ben and Peres, Yuval. Evolving sets and mixing. STOC '03, pp. 279-286. ACM, 2003.
26. Motwani, Rajeev and Raghavan, Prabhakar. Randomized algorithms. Cambridge University Press, 1995.
27. Schaeffer, S. E. Graph clustering. Computer Science Review, 1(1):27-64, 2007.
28. Shalev-Shwartz, Shai and Srebro, Nathan. SVM optimization: inverse dependence on training set size. In ICML, 2008.
29. Sherman, Jonah. Breaking the multicommodity flow barrier for o(√log n-approximations to sparsest cut. FOCS '09, pp. 363-372, 2009.
30. Shi, J. and Malik, J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000.
31. Sinclair, Alistair and Jerrum, Mark. Approximate counting, uniform generation and rapidly mixing markov chains. Information and Computation, 82(1):93-133, 1989.
32. Spielman, Daniel and Teng, Shang-Hua. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. STOC, 2004.
33. Spielman, Daniel and Teng, Shang-Hua. A local clustering algorithm for massive graphs and its application to nearly-linear time graph partitioning. CoRR, abs/0809.3232, 2008.
34. Wu, Xiao-Ming, Li, Zhenguo, So, Anthony Man-Cho, Wright, John, and Chang, Shih-Fu. Learning with partially absorbing random walks. In NIPS, 2012.
35. Zhu, Zeyuan Allen, Chen, Weizhu, Zhu, Chenguang, Wang, Gang, Wang, Haixun, and Chen, Zheng. Inverse time dependency in convex regularized learning. ICDM, 2009.