Graph stream classification using labeled and unlabeled graphs

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2013, Pages 398-409

  Pan, Shirui ^a   Zhu, Xingquan ^a   Zhang, Chengqi ^a   Yu, Philip S ^b  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b University of Illinois at Chicago   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX RELATIONSHIPS; CONCEPT DRIFTING; ENSEMBLE MODELS; GRAPH CLASSIFICATION; GRAPH OBJECTS; LABELED GRAPHS; REAL-WORLD GRAPHS; STREAM CLASSIFICATION;

REDUNDANCY;

GRAPHIC METHODS;

EID: 84881358578     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2013.6544842     Document Type: Conference Paper

References (27)

1. S. Raghavan and H. Garcia-Molina, "Representing web graphs," in Proc. of ICDE, Atlanta, USA, 2003.
2. C. Aggarwal, "On classification of graph streams," in Proc. of SDM, Arizona, USA, 2011.
3. S. Petrovic, M. Osborne, and V. Lavrenko, "Streaming first story detection with application to twitter," in Proc. of Human Language Technologies, Stroudsburg, USA, 2010.
4. M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis, "Frequent substructure-based approaches for classifying chemical compounds," IEEE Trans. on Knowl. and Data Eng., vol. 17, pp. 1036-1050, 2005. (Pubitemid 41259847)
5. H. Fei and J. Huan, "Boosting with structure information in the functional space: an application to graph classification," in Proc. of ACM SIGKDD, Washington DC, USA, 2010.
6. N. Ketkar, L. Holder, and D. Cook, "Empirical comparison of graph classification algorithms," in Proc. of IEEE CIDM, Nashville, 2009.
7. T. Kudo, E. Maeda, and Y. Matsumoto, "An application of boosting to graph classification," in Proc. of NIPS, Vancouver, Canada, 2004.
8. H. Saigo, S. Nowozin, T. Kadowaki, T. Kudo, and K. Tsuda, "gboost: a mathematical programming approach to graph classification and regression," Machine Learning, vol. 75, pp. 69-89, 2009.
9. W. Zhou and H. Xiong, "Volatile correlation computation: a checkpoint view," in Proc. of ACM SIGKDD, 2008, pp. 848-856.
10. K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, "Constrained kmeans clustering with background knowledge," in Proc. of ICML, 2001, pp. 577-584.
11. X. Yan and J. Han, "gspan: Graph-based substructure pattern mining," in Proc. of ICDM, Maebashi City, Japan, 2002.
12. X. Kong and P. Yu, "Semi-supervised feature selection for graph classification," in Proc. of ACM SIGKDD, Washington, DC, USA, 2010.
13. M. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical review E, vol. 69, no. 2, p. 026113, 2004.
14. C. Bron and J. Kerbosch, "Algorithm 457: finding all cliques of an undirected graph," C. of the ACM, vol. 16, no. 9, pp. 575-577, 1973.
15. Y. Freund and R. Schapire, "A desicion-theoretic generalization of online learning and an application to boosting," in Computational Learning Theory. Springer, 1995, pp. 23-37.
16. J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su, "Arnetminer: Extraction and mining of academic social networks," in Proceeding of ACM SIGKDD, 2008, pp. 990-998.
17. P. Mahe, N. Ueda, T. Akutsu, J. Pettet, and J. Vert, "Externsions of marginalized graph kernels," in Proc. of ICML, Alberta, Canada, 2004.
18. H. Kashima, K. Tsuda, and A. Inokuchi, Kernels for Graphs. Cambridge (Massachusetts): MIT Press, 2004, ch. In: Schlkopf B, Tsuda K, Vert JP, editors. Kernel methods in computational biology.
19. K. Riesen and H. Bunke, "Graph classification by means of lipschitz embedding," IEEE Trans. on SMC - B, vol. 39, pp. 1472-1483, 2009.
20. -, Graph Classification and Clustering Based on Vector Space Embedding. World Scientific Publishing Company, 2010.
21. H. Fei and J. Huan, "Structure feature selection for graph classification," in Proc. of ACM CIKM, California, USA, 2008.
22. N. Jin, C. Young, and W. Wang, "Graph classification based on pattern co-occurrence," in Proc. of ACM CIKM, Hong Kong, China, 2009.
23. M. Thoma, H. Cheng, A. Gretton, J. Han, H. Kriegel, A. Smola, L. Song, P. Yu, X. Yan, and K. Borgwardt, "Near-optimal supervised feature selection among frequent subgraphs," in Proc. of SDM, USA, 2009.
24. C. Aggarwal, "Outlier detection in graph streams," in Proc. of ICDE, Hannover, Germany, 2011.
25. P. Zhao, C. Aggarwal, and M. Wang, "gsketch: On query estimation in graph streams," in Proc. of VLDB, Istanbul, Turkey, 2012.
26. X. Zhu, P. Zhang, X. Lin, and Y. Shi, "Active learning from stream data using optimal weight classifier ensemble," IEEE Trans. on SMC - B, vol. 40, pp. 1607-1621, 2010.
27. H. Abdulsalam, D. Skillicorn, and P. Martin, "Classification using streaming random forests," IEEE TKDE, vol. 23, pp. 22-36, 2011.