🤖 AI Summary
Existing spatiotemporal graph models—such as GNNs and Transformers—typically adopt decoupled spatial and temporal modeling, limiting their ability to capture nontrivial structural dependencies inherent in graph topologies. To address this, we propose Cy2Mixer, a three-module architecture integrating temporal modeling, standard message passing, and a novel recurrent message-passing block (RMPB). The RMPB’s core innovation lies in explicitly constructing and aggregating cycle subgraphs to encode topological invariants; we theoretically prove its information complementarity with conventional message passing, thereby overcoming the limitations of spatiotemporal decoupling. Additionally, Cy2Mixer incorporates a gMLP backbone, gating mechanisms, and mathematically grounded topological representations. Extensive experiments on multiple spatiotemporal forecasting benchmarks demonstrate state-of-the-art performance, with significant improvements in prediction accuracy and generalization—particularly for traffic flow forecasting.
📝 Abstract
Graph Neural Networks (GNNs) and Transformer-based models have been increasingly adopted to learn the complex vector representations of spatio-temporal graphs, capturing intricate spatio-temporal dependencies crucial for applications such as traffic datasets. Although many existing methods utilize multi-head attention mechanisms and message-passing neural networks (MPNNs) to capture both spatial and temporal relations, these approaches encode temporal and spatial relations independently, and reflect the graph's topological characteristics in a limited manner. In this work, we introduce the Cycle to Mixer (Cy2Mixer), a novel spatio-temporal GNN based on topological non-trivial invariants of spatio-temporal graphs with gated multi-layer perceptrons (gMLP). The Cy2Mixer is composed of three blocks based on MLPs: A temporal block for capturing temporal properties, a message-passing block for encapsulating spatial information, and a cycle message-passing block for enriching topological information through cyclic subgraphs. We bolster the effectiveness of Cy2Mixer with mathematical evidence emphasizing that our cycle message-passing block is capable of offering differentiated information to the deep learning model compared to the message-passing block. Furthermore, empirical evaluations substantiate the efficacy of the Cy2Mixer, demonstrating state-of-the-art performances across various spatio-temporal benchmark datasets. The source code is available at url{https://github.com/leemingo/cy2mixer}.