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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Journal ArticleDOI
Link prediction in multiplex networks
Manisha Pujari,Rushed Kanawati +1 more
TL;DR: This work presents a new approach for co-authorship link prediction based on leveraging information contained in general bibliographical multiplex networks using supervised-machine learning based link prediction approach.
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Estimating potential trade links in the international crude oil trade: A link prediction approach
Qing Guan,Haizhong An,Haizhong An,Xiangyun Gao,Xiangyun Gao,Shupei Huang,Huajiao Li,Huajiao Li +7 more
TL;DR: In this article, a link prediction approach is introduced to explore potential trade links from the perspective of relations based on the topological attributes of countries. But the authors do not consider the role of crude oil trade between countries.
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Link prediction in dynamic social networks by integrating different types of information
Nahla Mohamed Ibrahim,Ling Chen +1 more
TL;DR: This work presents a method for link prediction in dynamic networks by integrating temporal information, community structure, and node centrality in the network, and achieves higher quality results than traditional methods.
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Discovering links among social networks
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Predicting missing links and their weights via reliable-route-based method.
TL;DR: In this paper, a reliable-route-based method was proposed to extend unweighted local similarity indices to weighted indices and propose a method to predict both the link existence and link weights accordingly.
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