  Biological spiking neurons with intrinsic dynamics underlie the powerful
representation and learning capabilities of the brain for processing multimodal
information in complex environments. Despite recent tremendous progress in
spiking neural networks (SNNs) for handling Euclidean-space tasks, it still
remains challenging to exploit SNNs in processing non-Euclidean-space data
represented by graph data, mainly due to the lack of effective modeling
framework and useful training techniques. Here we present a general spike-based
modeling framework that enables the direct training of SNNs for graph learning.
Through spatial-temporal unfolding for spiking data flows of node features, we
incorporate graph convolution filters into spiking dynamics and formalize a
synergistic learning paradigm. Considering the unique features of spike
representation and spiking dynamics, we propose a spatial-temporal feature
normalization (STFN) technique suitable for SNN to accelerate convergence. We
instantiate our methods into two spiking graph models, including graph
convolution SNNs and graph attention SNNs, and validate their performance on
three node-classification benchmarks, including Cora, Citeseer, and Pubmed. Our
model can achieve comparable performance with the state-of-the-art graph neural
network (GNN) models with much lower computation costs, demonstrating great
benefits for the execution on neuromorphic hardware and prompting neuromorphic
applications in graphical scenarios.

Neural and Evolutionary Computing;Machine Learning