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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Prediction of missing links based on community relevance and ruler inference
TL;DR: It is found that it is hard to predict the missing links if the two communities have little direct connections, so a novel algorithm which based on the community relevance and ruler inference is proposed to predict missing links and has more effective prediction accuracy.
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Prediction in complex systems: The case of the international trade network
TL;DR: Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future using a newly developed metric of product similarity which takes advantage of causality in the network evolution.
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An Ensemble Approach to Link Prediction
TL;DR: An ensemble enabled approach to scaling up link prediction is proposed, by decomposing traditional link prediction problems into subproblems of smaller size, each solved with latent factor models, which can be effectively implemented on networks of modest size.
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Link Prediction in Weighted Networks: A Weighted Mutual Information Model.
Boyao Zhu,Yongxiang Xia +1 more
TL;DR: A weighted model for undirected and weighted networks based on the mutual information of local network structures is presented, where link weights are applied to further enhance the distinguishable extent of candidate links.
Proceedings ArticleDOI
A General View for Network Embedding as Matrix Factorization
TL;DR: Experiments show that Matrix factorization based on a new proposed similarity measure and β-tuning strategy significantly outperforms existing matrix factorization approaches on a range of benchmark networks.
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