Link Prediction via controlling the leading eigenvector

TL;DR

This paper introduces a novel eigenvector-based approach to link prediction in networks, improving accuracy by balancing the influence of leading eigenvectors and outperforming existing local similarity methods.

Contribution

It presents a parameter-free algorithm that equalizes the contributions of top eigenvectors, enhancing link prediction performance without requiring tunable parameters.

Findings

The proposed algorithm outperforms traditional local similarity indices in real network tests.

Balancing eigenvector contributions improves prediction accuracy.

The method achieves competitive accuracy with lower computational complexity.

Abstract

Link prediction is a fundamental challenge in network science. Among various methods, similarity-based algorithms are popular for their simplicity, interpretability, high efficiency and good performance. In this paper, we show that the most elementary local similarity index Common Neighbor (CN) can be linearly decomposed by eigenvectors of the adjacency matrix of the target network, with each eigenvector's contribution being proportional to the square of the corresponding eigenvalue. As in many real networks, there is a huge gap between the largest eigenvalue and the second largest eigenvalue, the CN index is thus dominated by the leading eigenvector and much useful information contained in other eigenvectors may be overlooked. Accordingly, we propose a parameter-free algorithm that ensures the contributions of the leading eigenvector and the secondary eigenvector the same. Extensive experiments on real networks demonstrate that the prediction performance of the proposed algorithm is remarkably better than well-performed local similarity indices in the literature. A further proposed algorithm that can adjust the contribution of leading eigenvector shows the superiority over state-of-the-art algorithms with tunable parameters for its competitive accuracy and lower computational complexity.