Abstract
Link prediction is the problem of inferring future or missing relationships between nodes in a given network. This problem has attracted great attention since it has a large number of applications. In this problem, there is always some degree of uncertainty because the absence of a link between a pair of nodes may be the result of the non-existence of the link or the result of it not being observed but actually existing. In this paper, we propose a local link prediction technique that aggregates the observed evidence to estimate the probability of each possible non-observed link. We also show how our scalable link prediction technique achieves higher precision than other well-established local techniques in several networks from very different domains.
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
Similar content being viewed by others
References
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Martínez, V., Cano, C., Blanco, A.: ProphNet: a generic prioritization method through propagation of information. BMC Bioinform. 15(1), S5 (2014)
Pavlov, M., Ichise, R.: Finding experts by link prediction in coauthorship networks. FEWS 290, 42–55 (2007)
Huang, Z., Li, X., Chen, H.: Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital libraries, pp. 141–142. ACM (2005)
Martínez, V., Berzal, F., Cubero, J.C.: A survey of link prediction in complex networks. ACM Comput. Surv. 49(4), 69 (2016)
Massa, P., Salvetti, M., Tomasoni, D.: Bowling alone and trust decline in social network sites. In: Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2009, pp. 658–663. IEEE (2009)
Bu, D., Zhao, Y., Cai, L., Xue, H., Zhu, X., Lu, H., et al.: Topological structure analysis of the protein-protein interaction network in budding yeast. Nucleic Acids Res. 31(9), 2443–2450 (2003)
Guimera, R., Danon, L., Diaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 065103 (2003)
Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72(2), 027104 (2005)
Peri, S., Navarro, J.D., Amanchy, R., Kristiansen, T.Z., Jonnalagadda, C.K., Surendranath, V., et al.: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 13(10), 2363–2371 (2003)
Pajek datasets. http://vlado.fmf.uni-lj.si/pub/networks/data
Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Lü, L., Jin, C.H., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80(4), 046122 (2009)
Liu, Z., Zhang, Q.M., Lü, L., Zhou, T.: Link prediction in complex networks: a local naïve Bayes model. EPL (Europhys. Lett.) 96(4), 48007 (2011)
Acknowledgments
This work is partially supported by the Spanish Ministry of Economy and the European Regional Development Fund (FEDER), under grant TIN2012-36951 and the program “Ayudas para contratos predoctorales para la formación de doctores 2013” (grant BES-2013-064699).
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
Martínez, V., Berzal, F., Cubero, JC. (2017). Probabilistic Local Link Prediction in Complex Networks. In: Moral, S., Pivert, O., Sánchez, D., Marín, N. (eds) Scalable Uncertainty Management. SUM 2017. Lecture Notes in Computer Science(), vol 10564. Springer, Cham. https://doi.org/10.1007/978-3-319-67582-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-67582-4_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67581-7
Online ISBN: 978-3-319-67582-4
eBook Packages: Computer ScienceComputer Science (R0)