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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-319-67582-4_28
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