Nested subtree hash kernels for large-scale graph classification over streams

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 399-408

  Li, Bin ^a   Zhu, Xingquan ^a   Chi, Lianhua ^a   Zhang, Chengqi ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Data stream mining; Graph classification; Graph hash kernels; Nested subtree hashing

Indexed keywords

ANTICANCER ACTIVITIES; COMMON FEATURES; CONCEPT DRIFTS; CONVERGENCE RATES; DATA SET; DATA STREAM MINING; ENSEMBLE LEARNING; FAST SUBTREE KERNELS; FEATURE SPACE; GRAPH CLASSIFICATION; GRAPH HASH KERNELS; HIGHER RESOLUTION; KERNEL MATRICES; MULTI-RESOLUTIONS; PATTERN-LIST; REAL-WORLD; SUB TREES; SYNTHETIC GRAPHS;

ALGORITHMS; CHEMICAL COMPOUNDS; DATA MINING; STATISTICAL TESTS;

GRAPHIC METHODS;

EID: 84874076138     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.101     Document Type: Conference Paper

References (25)

1. H. Kashima, K. Tsuda, and A. Inokuchi, "Marginalized kernels between labeled graphs," in ICML, 2003, pp. 321-328.
2. T. Gärtner, P. Flach, and S. Wrobel, "On graph kernels: Hardness results and efficient alternatives," in COLT, 2003, pp. 129-143.
3. S. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt, "Graph kernels," Journal of Machine Learning Research, vol. 11, pp. 1201-1242, 2010.
4. N. Shervashidze and K. Borgwardt, "Fast subtree kernels on graphs," in NIPS, 2009, pp. 1660-1668.
5. N. Shervashidze, P. Schweitzer, E. J. van Leeuwen, K. Mehlhorn, and K. M. Borgwardt, "Weisfeiler-Lehman graph kernels," Journal of Machine Learning Research, vol. 12, pp. 2539-2561, 2011.
6. H. Wang, W. Fan, P. S. Yu, and J. Han, "Mining conceptdrifting data streams using ensemble classifiers," in KDD, 2003, pp. 226-235.
7. B. J. Weisfeiler and A. A. Leman, "A reduction of a graph to a canonical form and an algebra arising during this reduction," Nauchno-Technicheskaya Informatsia, vol. 2, no. 9, pp. 12-16, 1968.
8. P. Indyk, "Stable distributions, pseudorandom generators, embeddings and data stream computation," Journal of the ACM, vol. 53, no. 3, pp. 307-323, 2006.
9. D. Achlioptas, "Database-friendly random projections: Johnson-Lindenstrauss with binary coins," Journal of Computer and System Sciences, vol. 66, no. 4, pp. 671-687, 2003.
10. K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg, "Feature hashing for large scale multitask learning," in ICML, 2009, pp. 1113-1120.
11. S. N. Bernstein, The theory of Probabilities. Moscow: Gastehizdat Publishing House, 1946.
12. A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. Smola, "A kernel method for the two-sample problem," in NIPS, 2007, pp. 513-520.
13. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. on Intelligent Systems and Technology, vol. 2, pp. 27:1-27:27, 2011, software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm.
14. X. Yan and J. Han, "gSpan: Graph-based substructure pattern mining," in ICDM, 2002, pp. 721-724.
15. N. Wale, I. A. Watson, and G. Karypis, "Comparison of descriptor spaces for chemical compound retrieval and classification," Knowledge and Information Systems, vol. 14, no. 3, pp. 347-375, 2008.
16. X. Kong, W. Fan, and P. S. Yu, "Dual active feature and sample selection for graph classification," in KDD, 2011, pp. 654-662.
17. K. M. Borgwardt and H.-P. Kriegel, "Shortest-path kernels on graphs," in ICDM, 2005, pp. 74-81.
18. P. Mahé and J.-P. Vert, "Graph kernels based on tree patterns for molecules," Machine Learning, vol. 75, no. 1, pp. 3-35, 2009.
19. S. Hido and H. Kashima, "A linear-time graph kernel," in ICDM, 2009, pp. 179-188.
20. D. Haussler, "Convolution kernels on discrete structures," UC Santa Cruz, Tech. Rep. UCSC-CRL-99-10, 1999.
21. P. Domingos and G. Hulten, "Mining high-speed data streams," in KDD, 2000, pp. 71-80.
22. W. N. Street and Y. Kim, "A streaming ensemble algorithm (SEA) for large-scale classification," in KDD, 2001, pp. 377-382.
23. C. C. Aggarwal, "On classification of graph streams," in SDM, 2011, pp. 652-663.
24. Q. Shi, J. Petterson, G. Dror, J. Langford, A. Smola, and S. Vishwanathan, "Hash kernels for structured data," Journal of Machine Learning Research, vol. 10, pp. 2615-2637, 2009.
25. X. Wang, A. Smalter, J. Huan, and G. H. Lushington, "GHash: Towards fast kernel-based similarity search in large graph databases," in EDBT, 2009, pp. 472-480.