Predicting missing links and their weights via reliable-route-based method

Abstract

Link prediction aims to uncover missing links or predict the emergence of future relationships according to the current networks structure. Plenty of algorithms have been developed for link prediction in unweighted networks, with only a very few of them having been extended to weighted networks. Thus far, how to predict weights of links is important but rarely studied. In this Letter, we present a reliable-route-based method to extend unweighted local similarity indices to weighted indices and propose a method to predict both the link existence and link weights accordingly. Experiments on different real networks suggest that the weighted resource allocation index has the best performance to predict the existence of links, while the reliable-route-based weighted resource allocation index performs noticeably better on weight prediction. Further analysis shows a strong correlation for both link prediction and weight prediction: the larger the clustering coefficient, the higher the prediction accuracy.

5 pages, 4 tables

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• Experimental analyses on 2-hop-based and 3-hop-based link prediction algorithms
• A parametric approach to information filtering in complex networks: The Pólya filter
• Playing the role of weak clique property in link prediction: A friend recommendation model
• Reconstructing networks
• Explicitly Linking Regional Activation and Function Connectivity: Community Structure of Weighted Networks with Continuous Annotation
• Entropy-based approach to missing-links prediction
• Asymmetry of social interactions and its role in link predictability: the case of coauthorship networks
• Haar-Laplacian for directed graphs
• NEW: A Generic Learning Model for Tie Strength Prediction in Networks