Predicting missing links via correlation between nodes

TL;DR

This paper introduces a correlation coefficient-based method for link prediction that effectively estimates node similarity, especially in sparse networks, and outperforms existing methods when combined with resource allocation.

Contribution

It proposes a novel similarity measure using correlation coefficients and demonstrates its effectiveness when fused with resource allocation methods.

Findings

Correlation-based similarity improves link prediction accuracy.

Combined method outperforms existing approaches in sparse networks.

High-order path analysis enhances prediction effectiveness.

Abstract

As a fundamental problem in many different fields, link prediction aims to estimate the likelihood of an existing link between two nodes based on the observed information. Since this problem is related to many applications ranging from uncovering missing data to predicting the evolution of networks, link prediction has been intensively investigated recently and many methods have been proposed so far. The essential challenge of link prediction is to estimate the similarity between nodes. Most of the existing methods are based on the common neighbor index and its variants. In this paper, we propose to calculate the similarity between nodes by the correlation coefficient. This method is found to be very effective when applied to calculate similarity based on high order paths. We finally fuse the correlation-based method with the resource allocation method, and find that the combined method can substantially outperform the existing methods, especially in sparse networks.