Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Proposes combining causal ML with interpretable models to achieve competitive prediction performance and transparency on causal structures for decision support.
DNNs plus SHAP/SSHAP applied to 39 European bidding zones identify solar and gas as key price drivers and simulate a single-price EU market.
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A Step Towards Inherently Interpretable Causal Machine Learning Models For Decision Support
Proposes combining causal ML with interpretable models to achieve competitive prediction performance and transparency on causal structures for decision support.