AuCoGNN: Enhancing Graph Fairness Learning Under Distribution Shifts With Automated Graph Generation
Abstract: Graph neural networks (GNNs) have shown strong performance on graph-structured data but may inherit bias from training data, leading to discriminatory predictions based on sensitive ...
Abstract: Although supervised deep normal estimators have recently shown impressive results on synthetic benchmarks, their performance deteriorates significantly in real-world scenarios due to the ...
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