Continuous Spiking Graph Neural Networks

arXiv:2404.01897v1 Announce Type: cross
Abstract: Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, which is analyzed using ordinary differential equations (ODE). However, the implementation of CGNNs requires significant computational power, making them challenging to deploy on battery-powered devices. Inspired by recent spiking neural networks (SNNs), which emulate a biological inference …

abstract arxiv attention continuous cs.ai cs.lg cs.ne differential diffusion dynamics gnns graph graph neural networks however implementation inspiration networks neural networks novel ordinary propagation type