Random Walk on Multiple Networks

TL;DR

This paper introduces RWM, a flexible random walk method on multiple networks that leverages rich multi-source data for improved network analysis tasks like link prediction and community detection.

Contribution

It proposes a novel random walk algorithm for multiple networks, with theoretical analysis and efficient approximation methods, enhancing network analysis capabilities.

Findings

RWM effectively improves link prediction accuracy.

RWM demonstrates superior performance in network embedding tasks.

Theoretical guarantees ensure the convergence and efficiency of RWM.

Abstract

Random Walk is a basic algorithm to explore the structure of networks, which can be used in many tasks, such as local community detection and network embedding. Existing random walk methods are based on single networks that contain limited information. In contrast, real data often contain entities with different types or/and from different sources, which are comprehensive and can be better modeled by multiple networks. To take advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM. RWM is flexible and supports both multiplex networks and general multiple networks, which may form many-to-many node mappings between networks. RWM sends a random walker on each network to obtain the local proximity (i.e., node visiting probabilities) w.r.t. the starting nodes. Walkers with similar visiting probabilities reinforce each other. We theoretically analyze the convergence properties of RWM. Two approximation methods with theoretical performance guarantees are proposed for efficient computation. We apply RWM in link prediction, network embedding, and local community detection. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of RWM.