Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training graphs and testing graphs, inducing the degeneration of the generalization ability of ...
Abstract: This paper addresses the challenges of accurate state estimation in distribution grids, particularly the issues of sparse measurements and outliers in modern systems. A robust state ...
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