Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks
Keywords: Graph Neural Network, Traffic forecast, Spatio-Temporal Graph
TL;DR: We proposed the Cy2Mixer, a novel spatio-temporal GNN that leverages gated MLPs and cyclic subgraphs to capture spatial, temporal, and topological dependencies, outperforming existing methods on benchmark datasets.
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 https://anonymous.4open.science/r/cy2mixer-D5A9.
Submission Type: Full paper proceedings track submission (max 9 main pages).
Poster: png
Submission Number: 43
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