{"total":16,"rows":[{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2402.02827","title":"PowerGraph: A power grid benchmark dataset for graph neural networks","created":"2024-02-05","cited_by_count":19,"area":null},{"id":"2305.14814","title":"What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding","created":"2023-05-24","cited_by_count":12,"area":null},{"id":"2210.17281","title":"GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers","created":"2022-10-31","cited_by_count":11,"area":null},{"id":"2301.01835","title":"Deep Statistical Solver for Distribution System State Estimation","created":"2023-01-04","cited_by_count":10,"area":null},{"id":"2510.22048","title":"PF$\\Delta$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations","created":"2025-10-24","cited_by_count":7,"area":null},{"id":"2405.12295","title":"Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks","created":"2024-05-20","cited_by_count":3,"area":null},{"id":"2410.14109","title":"Improving Graph Neural Networks by Learning Continuous Edge Directions","created":"2024-10-18","cited_by_count":3,"area":null},{"id":"2404.03368","title":"Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity","created":"2024-04-04","cited_by_count":2,"area":null},{"id":"2406.08917","title":"Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning","created":"2024-06-13","cited_by_count":2,"area":null},{"id":"2407.12419","title":"Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions","created":"2024-07-17","cited_by_count":2,"area":null},{"id":"2507.06694","title":"Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study","created":"2025-07-09","cited_by_count":2,"area":null},{"id":"2308.05957","title":"Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights","created":"2023-08-11","cited_by_count":1,"area":null},{"id":"2506.06127","title":"Flow-Attentional Graph Neural Networks","created":"2025-06-06","cited_by_count":1,"area":null},{"id":"2602.18227","title":"Parameter-Efficient Domain Adaptation of Physics-Informed Self-Attention based GNNs for AC Power Flow Prediction","created":"2026-02-20","cited_by_count":1,"area":null},{"id":"2608.16494","title":"Graph Machine Learning: An Opportunity for Power Systems","created":"2026-08-17","cited_by_count":0,"area":null}],"next_cursor":null}