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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Proceedings ArticleDOI
A Distributed Link Prediction Algorithm Based on Clustering in Dynamic Social Networks
TL;DR: The experimental results show that the proposed algorithm has a higher prediction accuracy and lower time complexity, and is more scalable than traditional methods executed by a single machine.
Journal ArticleDOI
A group evolving-based framework with perturbations for link prediction
TL;DR: This work presents a group evolving-based characterization of node’s behavioral patterns, and proposes a model for link prediction which outperforms many classical methods with a decreasing computational time in large scales.
Proceedings ArticleDOI
Group Property Inference Attacks Against Graph Neural Networks
Xiuling Wang,Wendy Hui Wang +1 more
TL;DR: This work performs the first systematic study of group property inference attacks (GPIA) against GNNs, and shows that the target model trained on the graphs with or without the target property represents some dissimilarity in model parameters and/or model outputs which enables the adversary to infer the existence of the property.
Proceedings ArticleDOI
Link Prediction Based on Multi-steps Resource Allocation
Zhifeng Wu,Yaohui Li +1 more
TL;DR: A new similarity measure called Multi-Steps Resource Allocation (MSRA) is proposed, which uses the information of multi-steps neighbors to transmit the resource from one node to another node and shows the power of MSRA in link prediction.
Journal ArticleDOI
Energy-Efficient and Fault-Tolerant Evolution Models Based on Link Prediction for Large-Scale Wireless Sensor Networks
TL;DR: The experimental results demonstrate that the proposed models can generate SF-WSNs topologies with better fault-tolerance and higher energy-efficiency by comparing with a candidate clustering-based algorithm and other two SF enhancing algorithms.
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