用脉冲神经网络动态建模事件时空依赖，提升预测精度与泛化能力。
Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks
- 基于脉冲神经网络和突触可塑性，从事件数据中自动学习动态图结构。
- 在纽约出租车、911、Reddit等真实数据上，预测准确率优于现有方法。
- 适合处理复杂时序事件的建模，尤其在需要自适应图结构的场景中。
建模与预测时间点过程（TPPs）在神经科学、流行病学、金融和社会科学等领域至关重要。本文提出脉冲动态图网络（SDGN），利用脉冲神经网络（SNNs）的时间处理能力及尖峰时序依赖可塑性（STDP），动态估计潜在的时空功能图。与依赖预定义或静态图结构的现有方法不同，SDGN通过直接从事件数据中学习动态时空依赖，具备更强的通用性和鲁棒性。尽管在密集图和某些非高斯依赖关系下仍有局限，但其在纽约出租车、911、Reddit和Stack Overflow等合成与真实数据集上的评估显示，该方法显著提升预测准确率且保持计算高效。我们还通过消融实验验证了核心组件的贡献。
原文摘要 · Abstract (English)
Modeling and predicting temporal point processes (TPPs) is critical in domains such as neuroscience, epidemiology, finance, and social sciences. We introduce the Spiking Dynamic Graph Network (SDGN), a novel framework that leverages the temporal processing capabilities of spiking neural networks (SNNs) and spike-timing-dependent plasticity (STDP) to dynamically estimate underlying spatio-temporal functional graphs. Unlike existing methods that rely on predefined or static graph structures, SDGN adapts to any dataset by learning dynamic spatio-temporal dependencies directly from the event data, enhancing generalizability and robustness. While SDGN offers significant improvements over prior methods, we acknowledge its limitations in handling dense graphs and certain non-Gaussian dependencies, providing opportunities for future refinement. Our evaluations, conducted on both synthetic and real-world datasets including NYC Taxi, 911, Reddit, and Stack Overflow, demonstrate that SDGN achieves superior predictive accuracy while maintaining computational efficiency. Furthermore, we include ablation studies to highlight the contributions of its core components.
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