{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatial-temporal-bipartite-graph-attention","title":"Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting","arxiv_id":null,"date":"2024-04-25","proceeding":"Pacific-Asia Conference on Knowledge Discovery and Data Mining 2024 4","authors":["Dimuthu Lakmal","Kushani Perera","Renata Borovica-Gajic","Shanika Karunasekera"],"abstract":"Accurate traffic forecasting is pivotal for an efficient data-driven transportation system. The intricate nature of spatial-temporal dependencies and non-linearity present in traffic data has posed a significant challenge to the modeling of accurate traffic forecasting systems. Lately, there has been a significant effort to develop complex Spatial-Temporal Graph Neural Networks (STGNN) that predominantly utilize various Graph Neural Networks (GNN) and attention-based encoder-decoder architectures due to their ability to capture non-linear dependencies in spatial and temporal domains effectively. However, conventional GNNs limit explicit propagation of past information among nodes, while attention-based models such as transformers do not support finer-grained attention score distribution. In this study, we address the aforementioned issues and introduce a novel STGNN namely, Spatio-Temporal Bipartite Graph Attention Network (STBGAT) that allows explicit modeling of past information propagation among nodes. Further, we present a heterogeneous cross-attention mechanism in a transformer to compute finer-grained feature-wise attention distribution enabling the model to capture richer and more expressive temporal dependencies. Our experiments reveal that the proposed architecture outperforms the state-of-the-art approaches proposed in recent literature.","url_abs":"https://link.springer.com/chapter/10.1007/978-981-97-2253-2_6","url_pdf":"https://rdcu.be/emfpQ","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatial-temporal-bipartite-graph-attention","repo_url":"https://github.com/DimuthuLakmal/STBGAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"graph-attention","task_name":"Graph Attention"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}