Abstract
A crucial computational task for relational and network data is the “link prediction problem” which allows for example to discover unknown interactions between proteins to explain the mechanism of a disease in biological networks, or to suggest novel products for a customer in a e-commerce recommendation system. Most link prediction approaches however do not effectively exploit the contextual information available in the neighborhood of each edge. Here we propose to cast the problem as a binary classification task over the union of the pair of subgraphs located at the endpoints of each edge. We model the classification task using a support vector machine endowed with an efficient graph kernel and achieve state-of-the-art results on several benchmark datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Leicht, E.A., et al.: Vertex similarity in networks. Phys. Rev. E 73(2), 026120 (2006)
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS, vol. 6912, pp. 437–452. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23783-6_28
Miller, K., et al.: Nonparametric latent feature models for link prediction. In: Advances in Neural Information Processing Systems, pp. 1276–1284 (2009)
Von, M.C., et al.: Comparative assessment of large-scale data sets of proteinCprotein interactions. Nature 417(6887), 399–403 (2002)
Yamanishi, Y., et al.: Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics 21(suppl–1), i468–i477 (2005)
Rowies, S.: NIPS dataset, rwoeis/data.html (2002). http://www.cs.nyu.edu/
Lichtenwalter, R.N., et al.: New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243–252. ACM (2010)
Ghosn, F., et al.: The MID3 data set, 1993C2001: procedures, coding rules, and description. Conflict Manage. Peace Sci. 21(2), 133–154 (2004)
Ward, M.D., et al.: Disputes, democracies, and dependencies: a reexamination of the Kantian peace. Am. J. Polit. Sci. 51(3), 583–601 (2007)
Costa, F., De Grave, K.: Fast neighborhood subgraph pairwise distance kernel. In: Proceedings of the 26th International Conference on Machine Learning, pp. 255–262. Omnipress (2010)
Haussler, D.: Convolution kernels on discrete structures, vol. 646. Technical report, Department of Computer Science, University of California at Santa Cruz (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Tran-Van, D., Sperduti, A., Costa, F. (2017). Joint Neighborhood Subgraphs Link Prediction. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-70087-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70086-1
Online ISBN: 978-3-319-70087-8
eBook Packages: Computer ScienceComputer Science (R0)