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powergraph-gnn-research

Build a physics-guided GNN pipeline for PowerGraph PF/OPF/cascade tasks.

Updated Aug 27, 2026
Search Tags:#reproducibility#ssl#multitask#gnn#powergraph#physics-guided#cascade-explanation
One-click install
npx skills add https://github.com/mhdhazmi/GNNPowerSystem --skill powergraph-gnn-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: powergraph-gnn-research
Source: https://github.com/mhdhazmi/GNNPowerSystem/tree/main/skills/powergraph-gnn-research
Command: npx skills add https://github.com/mhdhazmi/GNNPowerSystem --skill powergraph-gnn-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torch-geometric, scipy, scikit-learn, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Research teams need a reproducible, physics-informed, topology-aware GNN framework tailored to power grids, enabling PF/OPF and cascade analysis on the PowerGraph benchmark.

Core Features & Use Cases

  • Data ingestion from the PowerGraph benchmark and conversion to PyG Data objects.
  • Self-supervised pretraining (grid-specific SSL) to bootstrap representations for low-label regimes.
  • Multi-task training with shared physics-guided encoder for PF, OPF, and cascade prediction, plus cascade explanation evaluation against ground-truth masks.
  • Reproducibility anchors including blocked splits, config templates, and reference docs.

Quick Start

Run the end-to-end PowerGraph GNN research pipeline to pretrain the encoder and run downstream PF/OPF/cascade experiments with explanation evaluation.

Frequently Asked Questions about powergraph-gnn-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a physics-guided GNN pipeline for power grid analysis?▼

You can build it by ingesting PowerGraph benchmark data into PyG Data objects, applying grid-specific self-supervised pretraining, and running multi-task training for PF, OPF, and cascade prediction using a shared physics-guided encoder.

What is self-supervised pretraining for graph neural networks in power systems?▼

Self-supervised pretraining for graph neural networks in power systems bootstraps grid-specific representations without labels. This SSL step helps GNN models perform better in low-label regimes before fine-tuning on downstream PF, OPF, or cascade prediction tasks.

Can I use PyTorch Geometric to train multi-task models for power flow and cascade prediction?▼

Yes, PyTorch Geometric supports training multi-task models for power flow and cascade prediction. A shared physics-guided encoder can simultaneously output PF, OPF, and cascade results, while cascade explanation evaluation validates predictions against ground-truth masks.

How do I evaluate GNN explanations for cascade prediction in power grids?▼

Evaluating GNN explanations for cascade prediction involves comparing the model's attention or attribution masks against ground-truth cascade masks. This pipeline includes cascade explanation evaluation to validate the physics-guided message-passing backbone's decision logic.

Do I need PyTorch and SciPy to run a reproducible GNN research pipeline on the PowerGraph benchmark?▼

Yes, you need PyTorch, PyTorch Geometric, SciPy, scikit-learn, and NumPy to run this GNN research pipeline. Reproducibility is maintained through blocked data splits, configuration templates, and reference documentation included in the pipeline.

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