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On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks., , , , , and . CoRR, (2023)On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods., , , , , and . CoRR, (2022)Network Design through Graph Neural Networks: Identifying Challenges and Improving Performance., and . CoRR, (2023)Reliable Graph Neural Network Explanations Through Adversarial Training., , , , and . CoRR, (2021)How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications., , , , and . KDD, page 2637-2647. ACM, (2022)Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design., , , , , , and . CoRR, (2020)Actionable Attribution Maps for Scientific Machine Learning., , , , , , and . CoRR, (2020)Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning., , , , , , and . CoRR, (2022)Generative Counterfactual Introspection for Explainable Deep Learning., , , and . GlobalSIP, page 1-5. IEEE, (2019)FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing., , , and . CoRR, (2022)