SSEL: spike-based structural entropic learning for spiking graph neural networks.

Spiking Neural Networks (SNNs) offer transformative, event-driven neuromorphic computing with unparalleled energy efficiency, representing a third-generation AI paradigm. Extending this paradigm to graph-structured data via Spiking Graph Neural Networks (SGNNs) promises energy-efficient graph cognition, yet existing SGNN architectures exhibit critical fragility under adversarial topology perturbations. To address this challenge, this study presents the Spike-based Structural Entropy Learning framework (SSEL), which introduces structural entropy theory into the learning objectives of SGNNs. The core innovation establishes structural entropy-guided topology refinement: By minimizing structural entropy, we derive a sparse topological graph that intrinsically prunes noisy edges while preserving critical low-entropy connections. To further enforce robustness, we develop an entropy-driven topological gating mechanism that restricts spiking message propagation exclusively to entropy-optimized edges, systematically eliminating adversarial pathways. Crucially, this co-design strategy synergizes two sparsity sources: Structural sparsity from the entropy-minimized graph topology and Event-driven sparsity from spike-based computation. This dual mechanism not only ensures exceptional robustness (64.58% accuracy vs. 30.14% baseline under 0.1 salt-and-pepper noise) but also enables ultra-low energy consumption, achieving 97.28% reduction compared to conventional GNNs while maintaining state-of-the-art accuracy (85.31% on Cora). This work demonstrates that the principled minimization of structural entropy is a powerful strategy for enhancing the robustness of Spiking Graph Neural Networks. The SSEL framework successfully mitigates the impact of adversarial topological perturbations while capitalizing on the energy-efficient nature of spike-based computation, which underscore the significant potential of combining information-theoretic graph principles with neuromorphic computing paradigms.