Link prediction in complex networks: A survey
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TLDR
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 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.read more
Citations
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A new link prediction in multiplex networks using topologically biased random walks
TL;DR: The goal of this paper is to propose an extended version of local random walk based on pure random walking for solving link prediction in the multiplex network, referred to as the Multiplex Local Random Walk (MLRW).
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To trade or not to trade: Link prediction in the virtual water network
TL;DR: A novel methodology for link prediction applied to the network of virtual water trade is proposed, able to reconstruct the network architecture without any prior knowledge of the network topology, using only the nodes and links attributes; it represents a general method that can be applied to other networks such as food or value trade networks.
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TL;DR: Based on the recommended collaborations of the data set, this study finds that neighbor-information-based approaches are more clustered on a 2-D multidimensional scaling map than topology-based ones.
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The dynamical structure of political corruption networks
TL;DR: In this paper, the authors analyzed political corruption scandals in Brazil over the past 27 years, focusing on the dynamical structure of networks where two individuals are connected if they were involved in the same scandal.
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Identifying important nodes by adaptive LeaderRank
Shuang Xu,Shuang Xu,Pei Wang +2 more
TL;DR: Simulations reveal that the new measure, called adaptive LeaderRank (ALR), which is a new member of the LR-family, can well balance between prediction accuracy and robustness and better adapt to the adjustment or local perturbations of network topologies, as compared with the existing measures.
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