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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Ranking in evolving complex networks
Hao Liao,Manuel Sebastian Mariani,Manuel Sebastian Mariani,Matúš Medo,Matúš Medo,Matúš Medo,Yi-Cheng Zhang,Zhou Mingyang +7 more
TL;DR: The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks and emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of real network traffic, prediction of future links, and identification of highly-significant nodes.
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Rate-optimal graphon estimation
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TL;DR: This paper establishes optimal rate of convergence for graphon estimation in a H\"{o}lder class with smoothness $\alpha$, which is, to the surprise, identical to the classical nonparametric rate.
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An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
TL;DR: This work provides an approach to predicting future links by applying the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are used in a linear combination of sixteen neighborhood and node similarity indices.
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Hiding individuals and communities in a social network
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Who to follow and why: link prediction with explanations
TL;DR: WTFW ("Who to Follow and Why"), a stochastic topic model for link prediction over directed and nodes-attributed graphs, is proposed, which not only predicts links, but for each predicted link it decides whether it is a "topical" or a "social" link, and depending on this decision it produces a different type of explanation.
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