GraphToolbox: A Configurable Framework for Controlled Benchmarking of Graph Neural Network Forecasting

arXiv:2609.24609v2 Announce Type: replace-cross Abstract: Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) represent these relations through a graph. Building a complete GNN forecasting experiment is nonetheless laborious, because graph construction, model selection, training, aggregation, and interpretation often require separate tools. We present GraphToolbox, an open-source Python framework that unifies these stages in one configuration-driven pipeline built on PyTorch Geometric. It offers data

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