FARW: A Feature-Aware Random Walk for node classification

Bastami, Sajad; Abdollahpouri, Alireza; Pir Mohammadiani, Rojiar

doi:10.48308/jicse.2025.237378.1039

FARW: A Feature-Aware Random Walk for node classification

Document Type : Original Article

Authors

Sajad Bastami

Alireza Abdollahpouri

Rojiar Pir mohammadiani

Faculty of Computer Engineering, University of Kurdistan, Sanandej, Iran

https://doi.org/10.48308/jicse.2025.237378.1039

Abstract

Graph-structured data, common in real-world applications, captures entities (nodes) and their relationships (edges). While traditional methods integrate node content and neighborhood information to represent nodes in a latent space, random walks—despite being grounded in graph topology—suffer from limitations such as bias towards high-degree nodes, slow convergence, and difficulty in handling disconnected components. To address these issues, we introduce the "Feature-Based Random Walk on Graphs" (FARW), an advanced method that prioritizes node similarity in random walks. Unlike traditional approaches, FARW determines movement based on node features, enabling a more comprehensive analysis of complex networks. This feature-based approach improves the representation of heterogeneous graphs and enhances performance on a variety of tasks. Moreover, FARW demonstrates greater robustness when the graph structure changes. Experiments on three datasets—Cora, PubMed, and CiteSeer—show that FARW outperforms traditional structure-based random walks and the Node2Vec method, achieving accuracies of 87%, 83%, and 65%, respectively. These results suggest that incorporating node features during random walks improves the efficiency and accuracy of network analysis across diverse applications

Keywords

Random Walk

Node Features

Complex Networks

Social Network Analysis

Subjects