HGNN Shield: Defending Hypergraph Neural Networks Against High-Order Structure Attack

Summary

The HGNN Shield paper proposes a defense framework for Hypergraph Neural Networks against structural attacks that disrupt message propagation. It combines a Hyperedge-Dependent Estimation module, which extends graph connectivity measures to hypergraphs, with a High-Order Shield module that detects, disconnects, and refines adversarial connections before the convolutional layers. Across six hypergraph datasets, the authors report an average performance improvement of 9.33% over other methods against targeted attacks.

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