HeTa: Relation-wise Heterogeneous Graph Foundation Attack Model
School of Cyberspace, Hangzhou Dianzi University（电子科技大学信息学院）；Data Security Governance Zhejiang Engineering Research Center, Hangzhou Dianzi University（浙江省数据安全治理工程研究中心）；Beihang University（北航）
Heterogeneous Graph Neural Networks (HGNNs) are vulnerable, highlighting the need for tailored attacks to assess their robustness and ensure security. However, existing HGNN attacks often require complex retraining of parameters to generate specific perturbations for new scenarios. Recently, foundation models have opened new horizons for the generalization of graph neural networks by capturing shared semantics across various graph distributions. This leads us to ask:Can we design a foundation attack model for HGNNs that enables generalizable perturbations across different HGNNs, and quickly adapts to new heterogeneous graphs (HGs)? Empirical findings reveal that, despite significant differences in model design and parameter space, different HGNNs surprisingly share common vulnerability patterns from a relation-aware perspective. Therefore, we explore how to design foundation HGNN attack criteria by mining shared attack units. In this paper, we propose a novel relation-wise heterogeneous graph foundation attack model, HeTa. We introduce a foundation surrogate model to align heterogeneity and identify the importance of shared relation-aware attack units. Building on this, we implement a serialized relation-by-relation attack based on the identified relational weights. In this way, the perturbation can be transferred to various target HGNNs and easily fine-tuned for new HGs. Extensive experiments exhibit powerful attack performances and generalizability of our method.
Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction
College of Informatics, Huazhong Agricultural University（华中农业大学信息学院）；Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University（华中农业大学农业生物信息学湖北省重点实验室）
Heterogeneous Graph Representation Learning with Relation Awareness
Beihang University（北京航空航天大学）；the University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）
Physics-Guided Dual-Stream Heterogeneous Graph Neural Network for Predicting Full-Field Structural Response of Stiffened Panels
Department of Mechanical Engineering, The University of British Columbia（不列颠哥伦比亚大学机械工程系）；Department of Civil Engineering, The University of British Columbia（不列颠哥伦比亚大学土木工程系）
Heterogeneous Hypergraph Embedding for Recommendation Systems
Griffith University（格里菲斯大学）；Hanoi University of Science and Technology（河内科技大学）；Ecole Polytechnique Federale de Lausanne（洛桑联邦理工学院）；The University of Queensland（昆士兰大学）
Department of Computer Applications in Science \& Engineering, BARCELONA Supercomputing Center, 08034 Barcelona, Spain Escuela Técnica Superior de Ingeniería (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain Human Centered AI, Data \& Software, LUXEMBOURG Institute of Science
