Outlier detection in graph streams

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2011, Pages 399-409

  Aggarwal, Charu C ^a   Zhao, Yuchen ^a   Yu, Philip S ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL CHALLENGES; MASSIVE NETWORKS; OUTLIER DETECTION; ROBUST MODELS; SAMPLING METHOD; SOCIAL NETWORKS; SPARSITY PROBLEMS; STRUCTURAL CONNECTIVITY; STRUCTURAL SUMMARY; UNDERLYING NETWORKS;

SOCIAL SCIENCES COMPUTING; TELECOMMUNICATION;

STATISTICS;

EID: 79957832478     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2011.5767885     Document Type: Conference Paper

References (21)

1. C. Aggarwal and H.Wang, Managing and Mining Graph Data, Springer, 2010.
2. C. Aggarwal, Social Network Data Analytics, Springer, 2011.
3. C. Aggarwal, Y. Xie, and P. Yu, "Gconnect: A connectivity index for massive disk-resident graphs," in PVLDB, vol. 2(1), pp. 862-873, 2009.
4. C. Aggarwal, Y. Zhao, and P. Yu, "On clustering graph streams," in SIAM Conf. on Data Mining, pp. 478-489, 2010.
5. C. Aggarwal, "On biased reservoir sampling in the presence of stream evolution," in VLDB Conf., pp. 607-618, 2006.
6. C. Aggarwal, J. Han, J. Wang, and P. Yu, "A framework for clustering evolving data streams," in VLDB Conf., pp. 81-92, 2003.
7. C. Aggarwal and P. Yu, "Outlier detection for high dimensional data," in SIGMOD Conf., pp. 37-46, 2001.
8. L. Akoglu, M. McGlohon, and C. Faloutsos, "Oddball: Spotting anomalies in weighted graphs," in PAKDD Conf., pp. 420-421, 2010.
9. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander, "Lof: Identifying density-based local outliers," in SIGMOD Conf., pp. 93-104, 2000.
10. G. N. Frederickson, "Data structures for on-line updating of minimum spanning trees, with applications," in SIAM J. Comput., vol. 14(4), pp. 781-798, 1985.
11. J. Gao, F. Liang, W. Fan, C. Wang, Y. Sun, and J. Han, "On community outliers and their efficient detection in information networks," in ACM KDD Conf., pp. 813-822, 2010.
12. M. Henzinger and V. King, "Randomized fully dynamic graph algorithms with polylogarithmic time per operation," in J. ACM, vol. 46(4), pp. 502-516, 1999.
13. W. Jin, Y. Jiang, W. Qian, and A. K. H. Tung, "Mining outliers in spatial networks," in DASFAA, pp. 156-170, 2006. (Pubitemid 43978640)
14. D. R. Karger, "Random sampling in cut, flow, and network design problems," in STOC, pp. 648-657, 1994.
15. B. W. Kernighan and S. Lin, "An efficient heuristic for partitioning graphs," in Bell Systems Technical Journal, vol. 49, pp. 291-307, 1970.
16. M.-S. Kim and J. Han, "A particle-and-density based evolutionary clustering method for dynamic networks," in PVLDB, vol. 2(1), pp. 622-633, 2009.
17. E. Knorr, R. Ng, and V. Tucakov, "Distance-based outliers: Algorithms and applications," in VLDB J., vol. 8(3-4), pp. 237-253, 2000.
18. S. Ramaswamy, R. Rastogi, and K. Shim, "Efficient algorithms for mining outliers from large data sets," in SIGMOD Conf., pp. 427-438, 2000.
19. Y. Sun, Y. Yu, and J. Han, "Ranking-based clustering of heterogeneous information networks with star network schema," in ACM KDD Conf., pp. 797-806, 2009.
20. J. S. Vitter, "Random sampling with a reservoir," in ACM Trans. Math. Softw., vol. 11(1), pp. 37-57, 1985.
21. Y. Zhou, H. Cheng, and J. X. Yu, "Graph clustering based on structural/attribute similarities," in PVLDB, vol. 2(1), pp. 718-729, 2009.