Link prediction in complex networks: A survey
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TL;DR
Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.
Abstract
Link prediction in complex networks has attracted increasing attention from\nboth physical and computer science communities. The algorithms can be used to\nextract missing information, identify spurious interactions, evaluate network\nevolving mechanisms, and so on. This article summaries recent progress about\nlink prediction algorithms, emphasizing on the contributions from physical\nperspectives and approaches, such as the random-walk-based methods and the\nmaximum likelihood methods. We also introduce three typical applications:\nreconstruction of networks, evaluation of network evolving mechanism and\nclassification of partially labelled networks. Finally, we introduce some\napplications and outline future challenges of link prediction algorithms.\n
