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arxiv: 1411.5118 · v2 · submitted 2014-11-19 · 💻 cs.SI · physics.soc-ph

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Link Prediction in Social Networks: the State-of-the-Art

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classification 💻 cs.SI physics.soc-ph
keywords linknetworkspredictionsocialdiscussedfuturelinksproblems
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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.

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  1. GravityGraphSAGE: Link Prediction in Directed Attributed Graphs

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    GravityGraphSAGE adapts GraphSAGE with a gravity-inspired decoder to outperform prior graph deep learning methods on directed link prediction across citation networks and 16 real-world graphs.