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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k-MIP attention enables linear-complexity graph transformers that approximate full attention arbitrarily closely and bounds GraphGPS expressivity via S-SEG-WL.
Heterogeneous Graph Attention Networks model intra-domain and inter-domain sensor relationships for short-term state forecasting in multi-domain power systems, outperforming baselines by 35.5% NRMSE on a hydroelectric case study.
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Learning Dynamic Stability Landscapes in Synchronization Networks
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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k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS
k-MIP attention enables linear-complexity graph transformers that approximate full attention arbitrarily closely and bounds GraphGPS expressivity via S-SEG-WL.
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Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study
Heterogeneous Graph Attention Networks model intra-domain and inter-domain sensor relationships for short-term state forecasting in multi-domain power systems, outperforming baselines by 35.5% NRMSE on a hydroelectric case study.