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.
Research highlights
►
Link prediction has found applications in network analysis and
reconstruction. ► We give a comprehensive survey on physical and machine
learning methods. ► We emphasize the statistical physical methods like
maximum likelihood methods. ► We outline promising directions for
further research and some open problems.
