Papers › Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks

Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks

29 Jan 2024arXiv:2401.15894archive 2025-07-28

Minho Lee, Yun Young Choi, Sun Woo Park, Seunghwan Lee, Joohwan Ko, Jaeyoung Hong

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}.

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Code

leemingo/cy2mixer officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Spatio-Temporal Forecasting Time Series Prediction Traffic Prediction

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Traffic Prediction	PeMS04	Cy2Mixer	12 Steps MAE	18.14	#4 of 12	Archive leaderboard	report
Traffic Prediction	PeMS07	Cy2Mixer	MAE@1h	19.45	#6 of 17	Archive leaderboard	report
Traffic Prediction	PeMS08	Cy2Mixer	MAE@1h	13.53	#6 of 13	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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