Progresses and Challenges in Link Prediction

Abstract

Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and metrics of link prediction, this Perspective will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning and others, mainly extracted from thousands of related publications in the last decade. Finally, this Perspective will outline some long-standing challenges for future studies.

arXiv comment45 pages, 1 table

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Cited by in corpus (7)

• Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
• Discriminating abilities of threshold-free evaluation metrics in link prediction
• The maximum capability of a topological feature in link prediction
• Link prediction with continuous-time classical and quantum walks
• Collaborative Filtering Approach to Link Prediction
• Understanding the network formation pattern for better link prediction
• Link Prediction via controlling the leading eigenvector