Local degree blocking model for link prediction in complex networks

Abstract

Recovering and reconstructing networks by accurately identifying missing and unreliable links is a vital task in the domain of network analysis and mining. In this article, by studying a specific local structure, namely a degree block having a node and its all immediate neighbors, we find it contains important statistical features of link formation for complex networks. We therefore propose a parameter-free local blocking (LB) predictor to quantitatively detect link formation in given networks via local link density calculations. The promising experimental results performed on six real-world networks suggest that the new index can outperform other traditional local similarity-based methods on most of tested networks. After further analyzing the scores' correlations between LB and two other methods, we find that the features of LB index are analogous to those of both PA index and short-path-based index, which empirically verify that large degree principle and short path principle simultaneously captured by the LB index are jointly driving link formation in complex networks.

References in corpus (20)

• Emergence of scaling in random networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Finding community structure in networks using the eigenvectors of matrices
• Link Prediction in Complex Networks: A Survey
• Defining and identifying communities in networks
• Hierarchical structure and the prediction of missing links in networks
• Clustering and preferential attachment in growing networks
• Predicting Missing Links via Local Information
• Growing Scale-Free Networks with Tunable Clustering
• What's in a crowd? Analysis of face-to-face behavioral networks
• Missing and spurious interactions and the reconstruction of complex networks
• Finding local community structure in networks
• Preferential attachment in the growth of social networks: the case of Wikipedia
• An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
• Preferential attachment in the protein network evolution
• Complex cooperative networks from evolutionary preferential attachment
• Link prediction in complex networks: a local na\"ıve Bayes model
• Potential Theory for Directed Networks
• Finding missing edges in networks based on their community structure
• Highly Dispersed Networks Generated by Enhanced Redirection

Cited by in corpus (3)

• Link Prediction via Matrix Completion
• Playing the role of weak clique property in link prediction: A friend recommendation model
• Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks