SGNNBench: A Holistic Evaluation of Spiking Graph Neural Networks on Large-scale Graphs

Graph Neural Networks (GNNs), as representative deep models designed for graph data, effectively learn graph topological information and push the performance boundaries across various graph tasks. However, the computational and memory burden of GNNs poses significant challenges for scaling to large real-world graphs. Spiking Graph Neural Networks (SGNNs), which integrate biologically plausible learning via unique spike-based neurons, have emerged as a promising energy-efficient alternative. Different layers communicate with sparse and binary spikes, which facilitates computation and storage of intermediate graph representations. Despite the proliferation of SGNNs proposed in recent years, there is no systematic benchmark to explore the basic design principles of these brain-inspired networks on the graph data. To bridge this gap, we present SGNNBench to quantify progress in the field of SGNNs. Specifically, we elaborately investigate the design space of SGNNs to facilitate the development of a general SGNN paradigm. Regarding efficiency, we empirically compare these baselines w.r.t. model size, memory usage and theoretical energy consumption to reveal previously overlooked energy bottlenecks. Furthermore, SGNNBench establishes a unified and reliable benchmark to comprehensively evaluate 9 state-of-the-art SGNNs across 20 datasets covering diverse graph settings and tasks.