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
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Influence me! Predicting links to influential users
TL;DR: This work adapted an influence maximization algorithm to find new influential users from the set of current influential users of the target user, and compared the results obtained with different metrics for link prediction and analyzed in which context these metrics obtained better results.
Proceedings ArticleDOI
Towards Better Evaluation for Dynamic Link Prediction
TL;DR: This work designs new, more stringent evaluation procedures for link prediction to dynamic graphs, which reflect real-world considerations, to better compare the strengths and weaknesses of methods.
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Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning
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A stochastic generative model of the World Trade Network.
TL;DR: A stochastic model is described that yields synthetic networks that closely mimic the properties of annual empirical data and combines two popular mechanisms of network generation: preferential attachment and multiplicative process.
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Accurate similarity index based on activity and connectivity of node for link prediction
TL;DR: A new similarity index, namely similarity based on activity and connectivity (SAC), is proposed, which performs link prediction more accurately and outperforms the compared baselines.
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