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arXiv AI By Zhichao Hou, Xitong Zhang, Wei Wang, Charu C. Aggarwal, Xiaorui Liu

Harnessing Trust in Directed Graphs: Redefining Robustness of Graph Learning

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The paper reexamines robustness in Graph Neural Networks (GNNs) by focusing on directed graphs, which have been largely ignored in prior work. It introduces a more realistic attack setting for directed graphs and proposes a simple message‑passing defense that can be added as a plug‑in layer. The authors demonstrate that leveraging the trust signals inherent in link directions can significantly enhance GNN robustness, achieving state‑of‑the‑art performance while maintaining strong clean accuracy against both transfer and adaptive attacks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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