aiqbench analysis
This research proposes a novel architecture that replaces standard self-attention mechanisms with an unrolled mixed-graph optimization algorithm to achieve both interpretability and efficiency in traffic forecasting. By integrating graph learning modules into an ADMM-based iterative process, the model drastically reduces parameter counts while maintaining competitive predictive performance against state-of-the-art schemes. The work advances the field of machine learning by demonstrating how structural constraints from spatial-temporal graphs can be effectively unrolled to create lightweight neural networks without sacrificing accuracy.
Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast
arXiv:2505.13102v4 Announce Type: replace-cross Abstract: Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions. 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 predict 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 design 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 periodically insert graph learning modules for $\mathcal{G}^u$ and $\mathcal{G}^d$ that play the role of self-attention. Experiments show that our unrolled networks achieve competitive traffic forecast performance as state-of-the-art prediction schemes, while reducing parameter counts drastically.
Abstract:Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions. 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 predict 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 design 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 periodically insert graph learning modules for $\mathcal{G}^u$ and $\mathcal{G}^d$ that play the role of self-attention. Experiments show that our unrolled networks achieve competitive traffic forecast performance as state-of-the-art prediction schemes, while reducing parameter counts drastically.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
arXiv:2505.13102 [cs.LG]
(or arXiv:2505.13102v4 [cs.LG] for this version)
From: Ji Qi Mr. [view email] [v1] Mon, 19 May 2025 13:32:34 UTC (388 KB) [v2] Sun, 12 Oct 2025 04:45:53 UTC (431 KB) [v3] Tue, 3 Feb 2026 08:42:35 UTC (5,419 KB) [v4] Thu, 11 Jun 2026 01:59:11 UTC (5,421 KB)
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