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 link directions using local directed path
TL;DR: Empirical analysis on real networks shows that the proposed Local Directed Path method can correctly predict link directions, which outperforms some local and global methods.
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Characterizing and modeling subnational virtual water networks of US agricultural and industrial commodity flows
Susana Garcia,Alfonso Mejia +1 more
TL;DR: Despite the high connectivity of the VWTNs, the presence of community structure indicates that large volumes of virtual water are traded regionally, suggesting the possibility of having hydroeconomic boundaries that differ from known physical boundaries, e.g., watersheds and aquifers.
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Attribute-Aware Graph Recurrent Networks for Scholarly Friend Recommendation Based on Internet of Scholars in Scholarly Big Data
TL;DR: This article proposes to design a scholarly friend recommendation system by taking advantages of network embedding and scholar attributes, and develops a novel graph recurrent neural framework to embed attributed scholar interactions within the model for recommendations.
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Predicting missing links via effective paths
Xuzhen Zhu,Hui Tian,Shi-Min Cai +2 more
TL;DR: A so-called effective path index (EP) is proposed in this paper to leverage effective influence of endpoints and strong connectivity in similarity calculation and shows a great improvement of performance via the authors' index.
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Multilevel learning based modeling for link prediction and users’ consumption preference in Online Social Networks
TL;DR: Novel direct and latent models to represent link prediction and a user’s consumption preferences in an OSN platform are proposed and a multilevel deep belief network learning-based model is introduced to achieve high accuracy.
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