Link prediction in complex networks: a local na\"ıve Bayes model

Abstract

Common-neighbor-based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In such method, each common neighbor of two nodes contributes equally to the connection likelihood. In this Letter, we argue that different common neighbors may play different roles and thus lead to different contributions, and propose a local na\"ıve Bayes model accordingly. Extensive experiments were carried out on eight real networks. Compared with the common-neighbor-based methods, the present method can provide more accurate predictions. Finally, we gave a detailed case study on the US air transportation network.

6 pages, 2 figures, 2 tables

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• Link Prediction in Complex Networks: A Mutual Information Perspective
• Link Prediction with Node Clustering Coefficient
• Link prediction based on path entropy
• Uncovering missing links with cold ends
• Reconstructing networks
• Improving local clustering based top-L link prediction methods via asymmetrical link clustering information
• Local degree blocking model for link prediction in complex networks
• Collaborative Filtering Approach to Link Prediction
• Performance of Local Information Based Link Prediction: A Sampling Perspective
• Interplay between Topology and Edge Weights in Real-World Graphs: Concepts, Patterns, and an Algorithm