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Recent findings have shown multiple graph learning models, such as graph classification and graph matching, are highly vulnerable to adversarial attacks, i.e. small input perturbations in graph structures and node attributes can cause the model failures. Existing defense techniques often defend specific attacks on particular multiple graph learning tasks. This paper proposes an attackagnostic graph-adaptive 1-Lipschitz neural network, ERNN, for improving the robustness of deep multiple graphdblp:conf/icml/ZhaoZZWJZJD021 fatcat:7thqnpakwvcm5emlwhxry2on4i