4 papers · 1 filter
Blackknife: Query-Free Black-Box Rewiring Attacks on Heterogeneous Graph Neural Networks
Honglin Gao, Junhao Ren, Lan Zhao +4
Heterogeneous graph neural networks (HGNNs) perform well on graphs with multiple node and relation types, but their robustness to attacks with restricted structural access remains…
HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs
Honglin Gao, Lan Zhao, Junhao Ren +2
Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less s…
HeteroBA: A Structure-Manipulating Backdoor Attack on Heterogeneous Graphs
Honglin Gao, Xiang Li, Lan Zhao +1
Heterogeneous graph neural networks (HGNNs) have recently drawn increasing attention for modeling complex multi-relational data in domains such as recommendation, finance, and soci…
Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification
Honglin Gao, Xiang Li, Yajuan Sun +1
Graph Neural Networks (GNNs) have attracted substantial interest due to their exceptional performance on graph-based data. However, their robustness, especially on heterogeneous gr…