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Life sciences · Preprint
arXiv · September 4, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint describes a novel graph representation method (MC-PA-RWF) that integrates physical edge states into random-walk fingerprints for power-grid cascading-failure classification. On three benchmark datasets, the method achieved balanced accuracy of 98.04–99.32% and outperformed several GNN baselines on F1 score by 1.60–5.84 percentage points, with reported statistical significance. The work is a technical contribution to computational power-systems modelling and has not undergone peer review.
Comparative algorithm benchmarking study. Power-grid graphs from PowerGraph benchmark systems; no further details on network sizes, operational conditions, or failure scenarios provided.. Intervention: Multi-Channel Physics-Aware Random Walk Fingerprints (MC-PA-RWF and MC-PA-RWF+) incorporating physical edge states into random-walk propagation.. Compared with: Graph neural network baselines (GCN, GAT, GINE, TransformerConv) and topology-only random walk fingerprints (RWF)..
MC-PA-RWF+ achieved balanced accuracy of 98.04% – 99.32% across the three largest evaluated settings F1-score improvement over strongest GNN baseline: 1.60 – 5.84 percentage points across all three systems Method shows competitive or superior performance to GCN, GAT, GINE, and TransformerConv baselines
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This is an unrefereed arXiv preprint presenting a machine-learning method for power-grid graph classification; it reports comparative accuracy metrics but lacks peer review and clinical or regulatory validation.
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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.
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