A Gated MLP Architecture for Learning Topological Dependencies in
  Spatio-Temporal Graphs
Yun Young Choi Minho Lee Sun Woo Park Seunghwan Lee Joohwan Ko
Graph Neural Networks (GNNs) and Transformer 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 message-passing block
for encapsulating spatial information, a cycle message-passing block for
enriching topological information through cyclic subgraphs, and a temporal
block for capturing temporal properties. 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 traffic benchmark datasets.