Graph-enhanced data recovery network (GEDR-Net): a robust approach for missing transaction data in power markets

The compliance and integrity of transaction data is paramount in power markets, yet the presence of missing or corrupted data can compromise market analysis and decision-making. To address this, we introduce GEDR-Net, a framework that combines Graph Neural Networks (GNNs) with Generative Adversarial Networks (GANs) for the recovery of missing transaction data. By harnessing the spatial relationships within the power market's graph structure, GEDR-Net predicts missing values while ensuring the authenticity of the recovered data through adversarial training. Our experimental findings suggest that GEDR-Net offers a promising approach to data recovery, showing improved performance compared to existing methods, especially in scenarios with substantial data incompleteness.