Latent feature kernels for link prediction on sparse graphs

IEEE Transactions on Neural Networks and Learning Systems

Volumn 23, Issue 11, 2012, Pages 1793-1804

  Nguyen, Canh Hao ^a   Mamitsuka, Hiroshi ^a  

^a KYOTO UNIVERSITY   (Japan)

Author keywords

Latent feature kernels; latent feature models; link prediction; sparse graphs

Indexed keywords

BINARY CLASSIFICATION PROBLEMS; COMPUTATIONAL COSTS; FEATURE MODELS; GENE REGULATORY NETWORKS; LATENT FEATURE KERNELS; LINK PREDICTION; PROTEIN-PROTEIN INTERACTIONS; SPARSE GRAPHS;

FORECASTING; GRAPH THEORY; PROTEINS; SEMANTICS;

PROBLEM SOLVING;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGY; COMPUTER SIMULATION; HUMAN; LEARNING; PREDICTIVE VALUE; PROCEDURES;

ALGORITHMS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; HUMANS; LEARNING; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS;

EID: 84876152499     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2012.2215337     Document Type: Article

References (37)

1. L. Getoor and B. Taskar, Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). Cambridge, MA: MIT Press, Nov. 2007.
2. A. Ben-Hur and W. S. Noble, "Kernel methods for predicting proteinprotein interactions," Bioinformatics, vol. 21, no. 1, pp. 38-46, 2005.
3. T. Kato, K. Tsuda, and K. Asai, "Selective integration of multiple biological data for supervised network inference," Bioinformatics, vol. 21, no. 10, pp. 2488-2495, 2005.
4. K. Bleakley, G. Biau, and J.-P. Vert, "Supervised reconstruction of biological networks with local models," Bioinformatics, vol. 23, no. 13, pp. 57-65, 2007.
5. D. Liben-Nowell and J. Kleinberg, "The link-prediction problem for social networks," J. Amer. Soc. Inf. Sci. Technol., vol. 58, pp. 1019-1031, May 2007.
6. K. Miller, T. Griffiths, and M. Jordan, "Nonparametric latent feature models for link prediction," in Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, Eds. Cambridge, MA: MIT Press, 2009, pp. 1276-1284.
7. J.-P. Vert and Y. Yamanishi, "Supervised graph inference," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2005, pp. 1433-1440.
8. E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing, "Mixed membership stochastic blockmodels," J. Mach. Learn. Res., vol. 9, pp. 1981-2014, Jun. 2008.
9. C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda, "Learning systems of concepts with an infinite relational model," in Proc. 21st Nat. Conf. Artif. Intell., vol. 1. 2006, pp. 381-388.
10. Z. Xu, V. Tresp, K. Yu, and H.-P. Kriegel, "Infinite hidden relational models," in Proc. 22nd Conf. Uncertain. Artif. Intell., 2006, pp. 1-8.
11. T. Griffiths and Z. Ghahramani, "Infinite latent feature models and the Indian buffet process," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2005.
12. D. J. Navarro and T. L. Griffiths, "A nonparametric Bayesian method for inferring features from similarity judgments," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2006, pp. 1033-1040.
13. N. Srebro, J. D. M. Rennie, and T. S. Jaakola, "Maximum-margin matrix factorization," in Advances in Neural Information Processing Systems, vol. 17. Cambridge, MA: MIT Press, Jul. 2005, pp. 1329-1336.
14. C. Ding, "Orthogonal nonnegative matrix tri-factorizations for clustering," in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2006, pp. 126-135.
15. R. I. Kondor and J. D. Lafferty, "Diffusion kernels on graphs and other discrete input spaces," in Proc. Int. Conf. Mach. Learn., 2002, pp. 315-322.
16. J. S. Kandola, J. Shawe-Taylor, and N. Cristianini, "Learning semantic similarity," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2002, pp. 657-664.
17. A. J. Smola and R. I. Kondor, "Kernels and regularization on graphs," in Proc. COLT, 2003, pp. 144-158.
18. T. Kato, H. Kashima, M. Sugiyama, and K. Asai, "Conic programming for multitask learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 1, pp. 957-968, Jul. 2010.
19. M. Shimbo, T. Ito, D. Mochihashi, and Y. Matsumoto, "On the properties of von Neumann kernels for link analysis," J. Mach. Learn., vol. 75, no. 1, pp. 37-67, 2009.
20. J. Kunegis and A. Lommatzsch, "Learning spectral graph transformations for link prediction," in Proc. 26th Annu. Int. Conf. Mach. Learn., 2009, pp. 561-568.
21. Y. Yamanishi, "Supervised bipartite graph inference," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2008, pp. 1841-1848.
22. B. Taskar, M. F. Wong, P. Abbeel, and D. Koller, "Link prediction in relational data," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2003.
23. J. Basilico and T. Hofmann, "Unifying collaborative and content-based filtering," in Proc. 21st Int. Conf. Mach. Learn., 2004, p. 9.
24. Z. Xu, V. Tresp, K. Yu, S. Yu, and H.-P. Kriegel, "Dirichlet enhanced relational learning," in Proc. 22nd Int. Conf. Mach. Learn., 2005, pp. 1004-1011.
25. M. A. Hasan and M. J. Zaki, "A survey of link prediction in social networks," in Social Network Data Analytics, C. Aggarwal, Ed. New York: Springer-Verlag, 2011, ch. 9, pp. 243-275.
26. C. H. Nguyen and H. Mamitsuka, "Kernels for link prediction with latent feature models," in Proc. Eur. Conf. Mach. Learn. Knowl. Disc. Databases, vol. 2. 2011, pp. 517-532.
27. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th Annu. Workshop Comput. Learn. Theory, 1992, pp. 144-152.
28. M. Deng, S. Mehta, F. Sun, and T. Chen, "Inferring domain-domain interactions from protein-protein interactions," in Proc. 6th Annu. Int. Conf. Comput. Biol., 2002, pp. 117-126.
29. M. E. J. Newman, "Clustering and preferential attachment in growing networks," Phys. Rev. E, vol. 64, no. 2, pp. 102-115, Jul. 2001.
30. F. Fouss, A. Pirotte, J. M. Renders, and M. Saerens, "Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation," IEEE Trans. Knowl. Data Eng., vol. 19, no. 3, pp. 355-369, May 2007.
31. G. H. Bakir, T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, and S. V. N. Vishwanathan, Predicting Structured Data (Neural Information Processing). Cambridge, MA: MIT Press, 2007.
32. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, "Support vector machine learning for interdependent and structured output spaces," in Proc. 21st Int. Conf. Mach. Learn., 2004, p. 104.
33. T. Hofmann, I. Tsochantaridis, and Y. Altun, "Learning over discrete output spaces via joint kernel functions," in Proc. Adv. Neural Inf. Process. Syst., Workshop Kernel Methods, 2002.
34. H. Kashima, S. Oyama, Y. Yamanishi, and K. Tsuda, "On pairwise kernels: An efficient alternative and generalization analysis," in Proc. 13th Pacific-Asia Conf. Adv. Knowl. Disc. Data Mining, 2009, pp. 1030-1037.
35. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.
36. L. Salwínski, C. S. Miller, A. J. Smith, F. K. Pettit, J. U. Bowie, and D. Eisenberg, "The database of interacting proteins: 2004 update," Nucl. Acids Res., vol. 32, no. 1, pp. 449-451, 2004.
37. S. Gama-Castro, H. Salgado, M. Peralta-Gil, A. Santos-Zavaleta, L. Muntildeiz-Rascado, H. Solano-Lira, V. Jimenez-Jacinto, V. Weiss, J. S. Garciacutea-Sotelo, A. Loacutepez-Fuentes, L. Porroacuten-Sotelo, S. Alquicira-Hernaacutendez, A. Medina-Rivera, I. Martiacutenez-Flores, K. Alquicira-Hernaacutendez, R. Martiacutenez-Adame, C. Bonavides-Martiacutenez, J. Miranda-Riacuteos, A. M. Huerta, A. Mendoza-Vargas, L. Collado-Torres, B. Taboada, L. Vega-Alvarado, M. Olvera, L. Olvera, R. Grande, E. Morett, and J. Collado-Vides, "RegulonDB version 7.0: Transcriptional regulation of escherichia coli k-12 integrated within genetic sensory response units (gensor units)," Nucl. Acids Res., vol. 39, no. 1, pp. 98-105, 2011.