Shahid Beheshti University Journal of Innovations in Computer Science and Engineering (JICSE) 2981-2135 1 2 2024 01 01 FARW: A Feature-Aware Random Walk for node classification 117 129 105280 10.48308/jicse.2025.237378.1039 EN Sajad Bastami Faculty of Computer Engineering, University of Kurdistan, Sanandej, Iran 0009-0008-1229-2887 Alireza Abdollahpouri Faculty of Computer Engineering, University of Kurdistan, Sanandej, Iran 0000-0003-3281-5944 Rojiar Pir Mohammadiani Faculty of Computer Engineering, University of Kurdistan, Sanandej, Iran 0000-0003-2998-1562 Journal Article 2024 10 27 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 https://jicse.sbu.ac.ir/article_105280_22d6cc80414f415c04a4da6d7d0f462a.pdf