FARW: A Feature-Aware Random Walk for node classification | ||
| Journal of Innovations in Computer Science and Engineering (JICSE) | ||
| مقاله 6، دوره 1، شماره 2، فروردین 2024، صفحه 76-88 اصل مقاله (660.2 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.48308/jicse.2025.237378.1039 | ||
| نویسندگان | ||
| Sajad Bastami؛ Alireza Abdollahpouri؛ Rojiar Pir mohammadiani* | ||
| Faculty of Computer Engineering, University of Kurdistan, Sanandej, Iran | ||
| چکیده | ||
| 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 | ||
| کلیدواژهها | ||
| Random Walk؛ Node Features؛ Complex Networks؛ Social Network Analysis | ||
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آمار تعداد مشاهده مقاله: 127 تعداد دریافت فایل اصل مقاله: 139 |
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