Effective graph classification based on topological and label attributes

Statistical Analysis and Data Mining

Volumn 5, Issue 4, 2012, Pages 265-283

  Li, Geng ^a   Semerci, Murat ^b   Yener, Bülent ^a   Zaki, Mohammed J ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b BOGAZICI UNIVERSITY   (Turkey)

Author keywords

Graph classification; Graph kernels; Graph mining; Topological attributes

Indexed keywords

DATA MINING; TOPOLOGY;

BENCHMARK DATASETS; COMPUTATIONAL OVERHEADS; DATA MINING TASKS; FEATURES VECTOR; GRAPH CLASSIFICATION; GRAPH KERNELS; GRAPH MINING; KERNEL-METHODS; SIMPLE++; TOPOLOGICAL ATTRIBUTE;

CLASSIFICATION (OF INFORMATION);

EID: 84864032087     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.11153     Document Type: Article

References (37)

1. T. Joachims, T. Hofmann, Y. Yue, and C.-N. Yu, Predicting structured objects with support vector machines, Commun ACM 52(11) (2009), 97-104.
2. L. Ralaivola, S. J. Swamidass, H. Saigo, and P. Baldi, Graph kernels for chemical informatics, Neural Netw 18 (2005), 1093-1110.
3. P. Mahè and J.-P. Vert, Graph kernels based on tree patterns for molecules, Mach Learn 75(1) (2009), 3-35.
4. K. M. Borgwardt, C. S. Ong, S. Schönauer, S. V. N. Vishwanathan, A. J. Smola, and H.-P. Kriegel, Protein function prediction via graph kernels, In ISMB (Supplement of Bioinformatics), 2005, 47-56.
5. C. Bilgin, C. Demir, C. Nagi, and B. Yener, Cell-graph mining for breast tissue modeling and classification, In 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, 5311-5314.
6. B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Cambridge, MA, MIT Press, 2001.
7. V. N. Vapnik, The Nature of Statistical Learning Theory, New York, Springer-Verlag, 1995.
8. T. Gärtner, P. Flach, and S. Wrobel, On graph kernels: hardness results and efficient alternatives, In 16th Annual Conference on Computational Learning Theory, 2003, 129-143.
9. H. Kashima, K. Tsuda, and A. Inokuchi, Marginalized kernels between labeled graphs, In International Conference on Machine Learning, 2003, 321-328.
10. K. M. Borgwardt and H.-P. Kriegel, Shortest-path kernels on graphs, In 5th IEEE International Conference on Data Mining, Washington, DC, 2005, 74-81.
11. T. Horvàth, T. Gärtner, and S. Wrobel, Cyclic pattern kernels for predictive graph mining, In 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004, 158-167.
12. J. Ramon and T. Gärtner, Expressivity versus efficiency of graphs kernels, In 1st International Workshop on Mining Graphs, Trees and Sequences, 2003.
13. N. Shervashidze and K. M. Borgwardt, Fast subtree kernels on graphs, In Advances in Neural Information Processing Systems, 2009, 1660-1668.
14. N. Shervashidze, S. V. Vishwanathan, T. H. Petri, K. Mehlhorn, and K. M. Borgwardt, Efficient graphlet kernels for large graph comparison, In 12th International Conference on Artificial Intelligence and Statistics, 2009, 488-495.
15. I. R. Kondor, N. Shervashidze, and K. M. Borgwardt, The graphlet spectrum, In International Conference on Machine Learning, Vol. 382, A. P. Danyluk, L. Bottou, and M. L. Littman, eds., 2009, 67.
16. M. Thoma, H. Cheng, A. Gretton, J. Han, H.-P. Kriegel, A. Smola, L. Song, P. S. Yu, X. Yan, and K. M. Borgwardt, Discriminative frequent subgraph mining with optimality guarantees, Stat Anal Data Mining 3(5) (2010), 302-318.
17. S. V. N. Vishwanathan, K. M. Borgwardt, and N. N. Schraudolph, Fast computation of graph kernels, In Neural Information Processing Systems, B. Schölkopf, J. Platt, and T. Hoffman, eds., Cambridge, MA, MIT Press, 2006, 1449-1456.
18. I. R. Kondor and J. Lafferty, Diffusion kernels on graphs and other discrete structures, In International Conference on Machine Learning, 2002, 315-322.
19. G. Li, M. Semerci, B. Yener, and M. J. Zaki, Graph classification via topological and label attributes, In 9th Workshop on Mining and Learning with Graphs (with SIGKDD), 2011.
20. P. Mahè, N. Ueda, T. Akutsu, J.-L. Perret, and J.-P. Vert, Extensions of marginalized graph kernels, In International Conference on Machine Learning, 2004.
21. S. V. N. Vishwanathan, N. N. Schraudolph, I. R. Kondor, and K. M. Borgwardt, Graph kernels, J Mach Learn Res 11 (2010), 1201-1242.
22. X. Yan and J. Han, gspan: Graph-based substructure pattern mining, In IEEE International Conference on Data Mining, 2002.
23. C. Meyer, Matrix Analysis and Applied Linear Algebra, Philadelphia, PA, SIAM, 2000.
24. Z. Bai, J. Demmel, J. Dongarra, A. Ruhe, and H. Vorst, Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide, Philadelphia, SIAM, 2000.
25. S. Nijssen and J. Kok, A quickstart in frequent structure mining can make a difference, Proceedings of the 2004 ACM SIGKDD international conference o n Knowledge discovery and data mining, 2004, 647-652.
26. E. Jones, T. Oliphant, P. Peterson, et al. SciPy: Open source scientific tools for Python, 2001. Available at
27. A. A. Hagberg, D. A. Schult, and P. J. Swart, Exploring network structure, dynamics, and function using NetworkX, In Proceedings of the 7th Python in Science Conference (SciPy2008), Pasadena, CA, 2008, 11-15.
28. C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, ACM Trans Intell Syst Technol 2 (2011), 27:1-27:27. Software available at
29. A. Debnath, R. L. de Compadre, G. Debnath, A. Shusterman, and C. Hansch, The structure-activity relationship of mutagenic aromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity, J Med Chem 34 (1991), 786-797.
30. N. Wale and G. Karypis, Comparison of descriptor spaces for chemical compound retrieval and classification, In IEEE International Conference on Data Mining, 2006.
31. P. Dobson and A. J. Doig, Distinguishing enzyme structures from non-enzymes without alignments, J Mol Biol 330(4) (2003), 771-783.
32. C. C. Bilgin, P. Bullough, G. E. Plopper, and B. Yener, Ecm-aware cell-graph mining for bone tissue modeling and classification, Data Mining Knowl Discov 20 (2010), 416-438.
33. C. Demir, S. H. Gultekin, and B. Yener, Learning the topological properties of brain tumors, IEEE/ACM Trans Comput Biol Bioinform 2(3) (2005), 262-270.
34. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Mach Learn 46(1) (2002), 389-422.
35. L. Lovász and K. Vesztergombi, Geometric representations of graphs, Paul Erdos and his Mathematics, 1999.
36. U. Brandes and D. Fleischer, Centrality measures based on current flow, STACS 2005, 2005, 533-544.
37. M. Newman, Networks: An Introduction, New York, NY, Oxford University Press, 2010.