Link Prediction Based on Local Random Walk

TL;DR

This paper introduces a local random walk method for link prediction in large, sparse networks, achieving high accuracy with lower computational costs compared to existing random-walk-based approaches.

Contribution

The paper proposes a novel local random walk approach that improves prediction accuracy and reduces computational complexity in large-scale network link prediction.

Findings

Achieves competitive or better prediction accuracy than existing methods.

Reduces computational complexity in large networks.

Effective in sparse network scenarios.

Abstract

The problem of missing link prediction in complex networks has attracted much attention recently. Two difficulties in link prediction are the sparsity and huge size of the target networks. Therefore, the design of an efficient and effective method is of both theoretical interests and practical significance. In this Letter, we proposed a method based on local random walk, which can give competitively good prediction or even better prediction than other random-walk-based methods while has a lower computational complexity.