Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification

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On September 7, 2026, arXiv cs.LG published the paper “Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification”. This study proposed a physics-aware random walk fingerprinting method aimed at improving the scalability of power grid graph classification.

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Key entitiesKEY ENTITIES

MC-PA-RWFPowerGraph

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SignalsSIGNALS

Keyword heat

PowerGraph1
MC-PA-RWF1

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A arXiv cs.LG en 2026-09-07 12:00

本文提出一种名为 MC-PA-RWF 的轻量级图表示框架，将物理边状态引入随机游走传播过程，用于电力系统的级联故障分类。该方法通过构建多通道加权图提取特定指纹并拼接为紧凑向量。在三个 PowerGraph 基准系统上的实验表明，MC-PA-RWF+ 在最大评估设置下达到约 98.04% - 99.32% 的平衡准确率，相比最强 GNN 基线提升失败类 F1 值 1.60 -- 5.84 个百分点，且在所有三个系统中具有统计显著性增益。