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 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%2C which is analyzed using ordinary differential equations (ODE). However%2C the implementation of CGNNs requires significant computational power%2C making them challenging to deploy on battery-powered devices. Inspired by recent spiking neural networks (SNNs)%2C which emulate a biological inference process and provide an energy-efficient neural architecture%2C we incorporate the SNNs with CGNNs in a unified framework%2C named Continuous Spiking Graph Neural Networks (COS-GNN). We employ SNNs for graph node representation at each time step%2C which are further integrated into the ODE process along with time. To enhance information preservation and mitigate information loss in SNNs%2C we introduce the high-order structure of COS-GNN%2C which utilizes the second-order ODE for spiking representation and continuous propagation. Moreover%2C we provide the theoretical proof that COS-GNN effectively mitigates the issues of exploding and vanishing gradients%2C enabling us to capture long-range dependencies between nodes. Experimental results on graph-based learning tasks demonstrate the effectiveness of the proposed COS-GNN over competitive baselines.?quality=80&w=800)
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