GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting

arXiv:2609.24609 · cs.LG, cs.AI · Submitted 2026-09-21 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-21

Updated: 2026-09-21

Code: https://github.com/Dralliag/opera-python

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Aggregation — 28× in this paper · explained in 1 episode
• Interpretability — 13× in this paper · explained in 15 episodes
• Graph Construction — 11× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• GNNs — 3× in this paper · explained in 2 episodes
• Heterogeneous Graph — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Graph Neural Networks for Electricity Load Forecasting

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