Link prediction based on path entropy

Abstract

Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, we first study the information entropy or uncertainty of a path using the information theory. Then we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths. Empirical experiments demonstrate that PE index outperforms the mainstream link predictors.

16 pages, 1 figure

References in corpus (12)

• Critical phenomena in complex networks
• Hierarchical structure and the prediction of missing links in networks
• Predicting Missing Links via Local Information
• An information-theoretic framework for resolving community structure in complex networks
• Effective and Efficient Similarity Index for Link Prediction of Complex Networks
• The entropy of randomized network ensembles
• Reconstructing propagation networks with natural diversity and identifying hidden sources
• Entropy Rate of Diffusion Processes on Complex Networks
• Link Prediction in Complex Networks: A Mutual Information Perspective
• Maximal-entropy random walks in complex networks with limited information
• Epidemic spreading driven by biased random walks
• Traffic-driven epidemic spreading in correlated networks

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• Multiple predator based capture process on complex networks
• Node similarity distribution of complex networks and its application in link prediction