A Survey of Link Prediction in Complex Networks

Author:

Martínez Víctor1ORCID,Berzal Fernando1,Cubero Juan-Carlos1

Affiliation:

1. University of Granada, Granada, Spain

Abstract

Networks have become increasingly important to model complex systems composed of interacting elements. Network data mining has a large number of applications in many disciplines including protein-protein interaction networks, social networks, transportation networks, and telecommunication networks. Different empirical studies have shown that it is possible to predict new relationships between elements attending to the topology of the network and the properties of its elements. The problem of predicting new relationships in networks is called link prediction. Link prediction aims to infer the behavior of the network link formation process by predicting missed or future relationships based on currently observed connections. It has become an attractive area of study since it allows us to predict how networks will evolve. In this survey, we will review the general-purpose techniques at the heart of the link prediction problem, which can be complemented by domain-specific heuristic methods in practice.

Funder

Ministry of Education of Spain under the program “Ayudas para contratos predoctorales para la formación de doctores 2013”

Spanish Ministry of Economy and the European Regional Development Fund

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference86 articles.

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2. Vladimir Batagelj and Andrej Mrvar. 2006. Pajek datasets. Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/data. Vladimir Batagelj and Andrej Mrvar. 2006. Pajek datasets. Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/data.

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