Link prediction in complex networks: A survey

Linyuan Lü and Tao Zhou

Physica A: Statistical Mechanics and its Applications, 2011, vol. 390, issue 6, 1150-1170

Abstract: Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

Keywords: Link prediction; Complex networks; Node similarity; Maximum likelihood methods; Probabilistic models (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (233)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:390:y:2011:i:6:p:1150-1170

DOI: 10.1016/j.physa.2010.11.027

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