{"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/lightweight-transformer-via-unrolling-of","title":"Lightweight Transformer via Unrolling of Mixed Graph Algorithms for Traffic Forecast","arxiv_id":"2505.13102","date":"2025-05-19","proceeding":null,"authors":["Ji Qi","Tam Thuc Do","Mingxiao Liu","Zhuoshi Pan","Yuzhe Li","Gene Cheung","H. Vicky Zhao"],"abstract":"To forecast traffic with both spatial and temporal dimensions, we unroll a mixed-graph-based optimization algorithm into a lightweight and interpretable transformer-like neural net. Specifically, we construct two graphs: an undirected graph $\\mathcal{G}^u$ capturing spatial correlations across geography, and a directed graph $\\mathcal{G}^d$ capturing sequential relationships over time. We formulate a prediction problem for the future samples of signal $\\mathbf{x}$, assuming it is \"smooth\" with respect to both $\\mathcal{G}^u$ and $\\mathcal{G}^d$, where we design new $\\ell_2$ and $\\ell_1$-norm variational terms to quantify and promote signal smoothness (low-frequency reconstruction) on a directed graph. We construct an iterative algorithm based on alternating direction method of multipliers (ADMM), and unroll it into a feed-forward network for data-driven parameter learning. We insert graph learning modules for $\\mathcal{G}^u$ and $\\mathcal{G}^d$, which are akin to the self-attention mechanism in classical transformers. Experiments show that our unrolled networks achieve competitive traffic forecast performance as state-of-the-art prediction schemes, while reducing parameter counts drastically. Our code is available in https://github.com/SingularityUndefined/Unrolling-GSP-STForecast.","url_abs":"https://arxiv.org/abs/2505.13102v1","url_pdf":"https://arxiv.org/pdf/2505.13102v1.pdf","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":"lightweight-transformer-via-unrolling-of","repo_url":"https://github.com/singularityundefined/unrolling-gsp-stforecast","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-learning","task_name":"Graph Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}