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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Predicting Positive and Negative Relationships in Large Social Networks.
TL;DR: This paper proposes a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory and shows that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates.
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Temporal similarity metrics for latent network reconstruction: The role of time-lag decay
Hao Liao,Ming-Kai Liu,Manuel Sebastian Mariani,Manuel Sebastian Mariani,Zhou Mingyang,Xing-Tong Wu +5 more
TL;DR: This work introduces new node-node temporal similarity metrics based on static similarity metrics commonly used in the link prediction literature, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time.
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Integrated anchor and social link predictions across multiple social networks
TL;DR: A unified link prediction framework, collective link fusion (CLF) is proposed in this paper, which consists of two phases: step (1) collective link prediction of anchor and social links with positive and unlabeled learning techniques, and step (2) propagation of predicted links across the partially aligned “probabilistic networks” with collective random walk.
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Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach.
TL;DR: A computational model based on a heat diffusion algorithm which can predict the association between tumor samples and genes and it is demonstrated that the gene-gene interaction scores could improve the predictive power of the heat diffusion model to predict the links between tumor sample and genes.
Journal ArticleDOI
Overlapping influence inspires the selection of multiple spreaders in complex networks
TL;DR: By considering the overlapping influences (coupling effects), a novel framework is proposed to upgrade the collective influence of multiple spreaders to select influential spreaders, yet with low overlapping influences.
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