Link prediction in social networks: the state-of-the-art

By Peng Wang, Baowen Xu, Yurong Wu, Xiaoyu Zhou. Science China Information Sciences, 2014

606 citations recorded by the source corpus.

In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/cfe701c64e4ff1d7e5b58a194b02d595b65ce872

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Citations in the corpus

A Survey on Deep Learning (2018, methodology): “Deep-learning-based approaches are applied to improve the performance of the prediction and to tackle problems such as scalability and nonlinearity [163].”
A Survey of Link Prediction in Complex Networks (2016, methodology): “Different reviews about this topic have been previously published and have inﬂu-enced this work [Liben-Nowell and Kleinberg 2007; Al Hasan and Zaki 2011; L¨u and Zhou 2011; Wang et al. 2015].”