面向可扩展电网图分类的物理感知随机游走指纹
Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification
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中文总结 AI 辅助
本文提出多通道物理感知随机游走指纹（MC-PA-RWF），将物理边状态引入随机游走传播，在PowerGraph基准系统上较仅拓扑的RWF显著改进，且性能优于多数GNN基线，实现高平衡精度与故障类F1提升。
中文摘要 AI 辅助
PowerGraph等最新基准提供了用于级联故障分类的大规模电网图集合。图神经网络（GNN）在该任务上取得了出色的预测性能，但通常需要端到端训练和特定模型调优，且其潜在表示难以与具有物理意义的传播模式关联。随机游走指纹（RWF）是一种可扩展且可解释的替代方案，但现有变体主要关注拓扑和节点级信息，未利用游走动力学中与电网相关的运营边状态。本文针对电力系统提出多通道物理感知随机游走指纹（MC-PA-RWF），这是一种轻量级图级表示框架，将物理边状态引入随机游走传播。该方法从领域相关属性构建多个边加权通道，从每个加权图中提取通道特定指纹，并将所得向量连接为紧凑表示。在三个PowerGraph基准系统上的实验表明，与仅考虑拓扑的RWF相比，该方法实现了显著改进，且与图卷积网络（GCN）、图注意力网络（GAT）、带边特征的图同构网络（GINE）及基于Transformer的图卷积网络（TransformerConv）等强大GNN基线的平衡精度具有竞争力。在评估的最大设置下，节点-边扩展版MC-PA-RWF+实现约98.04%至99.32%的平衡精度，且将故障类F1值较最强GNN基线提升1.60至5.84个百分点，在三个系统上均具有统计显著的增益。
英文摘要
Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure classification. Graph neural networks (GNNs) achieve strong predictive performance on this task, but typically require end-to-end training and model-specific tuning, while their latent representations can be difficult to relate to physically meaningful propagation patterns. Random Walk Fingerprints (RWF) offer a scalable and interpretable alternative, but existing variants primarily emphasise topology and node-level information, leaving grid-relevant operational edge states in the walk dynamics. We propose Multi-Channel Physics-Aware Random Walk Fingerprints (MC-PA-RWF) for power systems, a lightweight graph-level representation framework that introduces physical edge states into random-walk propagation. The method constructs multiple edge-weighted channels from domain-relevant attributes, extracts a channel-specific fingerprint from each weighted graph, and concatenates the resulting vectors into a compact representation. Experiments on three \textit{PowerGraph} benchmark systems show substantial improvements over topology-only RWF and competitive balanced accuracy against strong GNN baselines, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Isomorphism Networks with edge features (GINE), and Transformer-based Graph Convolutional Networks (TransformerConv). At the largest evaluated settings, the node-edge extension MC-PA-RWF+ achieves around 98.04% - 99.32% balanced accuracy and improves failure-class F1 over the strongest GNN baseline by 1.60 -- 5.84 percentage points, with statistically significant gains across all three systems.
发表机构
- Deakin University（迪肯大学）
- School of Information Technology（信息技术学院）
- Deakin Cyber Research & Innovation Hub（迪肯网络研究与创新中心）
机构由 AI 辅助整理，请以论文原文为准。
