Spiking graph neural networks: A survey

Spiking Neural Networks (SNNs) offer a biologically grounded alternative to conventional Artificial Neural Networks by replacing continuous activations with discrete, asynchronous spike events, while Graph Neural Networks (GNNs) provide a principled way to learn over non-Euclidean relational data. This paper surveys the emerging family of Spiking Graph Neural Networks (SGNNs), which unify these paradigms under a common modular template of a graph convolution backbone, a spike encoding stage, one or more Leaky Integrate-and-Fire (LIF) neuron layers, and a classification readout. Ten representative architectures are compared along four axes: the neuron model, the graph convolution mechanism, the treatment of time (static versus dynamic graphs), and the underlying representation space (Euclidean versus Riemannian). We further review training algorithms, showing that the non-differentiable spikefunction has driven three complementary solutions: surrogate-gradient Backpropagation Through Time, implicit or manifold-based differentiation with O(1) memory, and biologically-local Spike-Timing Dependent Plasticity. To demonstrate practical utility, we also present a case study applying an GNN inspired by SiGNN to malicious DNS-over-HTTPS traffic detection, where per-timestamp graphs are processed by graph convolutional layers followed by LIF-based spiking layers. The results indicate that SGNNs remain resilient to the strong class imbalance of the benchmark, supporting their viability for cybersecurity classification. Taken together, the surveyed work suggests that spiking graph models can inherit the energy benefits of neuromorphic hardware without sacrificing the expressive power of modern graph learning.