Link Prediction via Matrix Completion

TL;DR

This paper presents a novel link prediction method using robust PCA to estimate missing entries in adjacency matrices, effectively handling both sparse and dense networks across various real-world datasets.

Contribution

It introduces a robust PCA-based approach for link prediction that outperforms existing algorithms, especially in dense network scenarios.

Findings

Effective in dense and sparse networks

Significantly improves prediction accuracy

Works well across different types of real networks

Abstract

Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On one hand, our algorithm is based on the sparsity and low rank property of the matrix, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whatever it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved comparing with many state-of-the-art algorithms.