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Found 4 papers, 1 papers with code
Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
Learning to represent and simulate the dynamics of physical systems is a crucial yet challenging task.
Robust Graph Neural Networks via Unbiased Aggregation
The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks despite the existence of numerous defenses.
Automated Polynomial Filter Learning for Graph Neural Networks
Polynomial graph filters have been widely used as guiding principles in the design of Graph Neural Networks (GNNs).
Can Directed Graph Neural Networks be Adversarially Robust?
This work presents the first investigation into the robustness of GNNs in the context of directed graphs, aiming to harness the profound trust implications offered by directed graphs to bolster the robustness and resilience of GNNs.
