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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Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network
TL;DR: A community detection approach, the density-degree centrality-jaccard-k-means method (DDJKM), which can avoid the problem of random selection of initial cluster centers in conventional k-mean clustering algorithms, and is practical a approach for the detection of communities with large network datasets.
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SI-spreading-based network embedding in static and temporal networks.
TL;DR: Li et al. as discussed by the authors proposed to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths, which is more applicable to large-scale networks.
Journal ArticleDOI
Link Prediction Based on Deep Convolutional Neural Network
TL;DR: A link prediction method based on deep convolutional neural network that constructs a model of the residual attention network to capture the link structure features from the sub-graph is proposed.
Proceedings ArticleDOI
A vertex similarity index using community information to improve link prediction accuracy
TL;DR: A novel similarity index for link prediction which combines the topology information and community information is proposed and the experiment results shown that the proposed approach can improve the accuracy of link prediction no matter which community detection algorithm is used.
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Adversarial Robustness of Similarity-Based Link Prediction
TL;DR: Focusing on similarity metrics using only local information, it is shown that the problem is NP-Hard for both players, and two principled and efficient approaches for solving it approximately are devised.
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