HyperRoGMAE: Multi-Scale Semantic Hypergraphs for Robust Graph Masked Autoencoder against Structural Attacks

Graph Masked AutoEncoders (GMAEs) propagate contextual information through the observed pairwise topology to reconstruct masked graph elements. This paradigm makes them vulnerable to structural attacks that corrupt the observed topology. Existing robust GMAEs mitigate this vulnerability through adversarial masking, structural augmentation, and edge-level filtering or reweighting. However, their contextual modeling relies primarily on pairwise relations and thus lacks multi-scale, higher-order semantic context for robust reconstruction under structural attacks. To this end, we propose $HyperRoGMAE$, a $Hyper$graph-guided $Ro$bust $G$raph $M$asked $A$uto$E$ncoder that constructs a multi-scale semantic hypergraph for masked representation learning. Specifically, HyperRoGMAE first constructs a multi-scale semantic hypergraph and estimates semantic reliability based on assignment margins and cross-view stability. It then pretrains a hypergraph teacher that encodes masked views and provides reliability-weighted higher-order guidance. During student pretraining, it attenuates suspicious edges according to feature similarity and hypergraph-membership consistency. Finally, it introduces a node-aware adaptive fusion strategy to fuse graph and semantic representations and employs dual reconstruction objectives to preserve their complementary information. Extensive experiments on five datasets demonstrate the effectiveness of the proposed HyperRoGMAE under four structural attacks.