Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have made remarkable progress in many physical scenarios, especially in communication applications. Despite achieving great success, the privacy issue of such models has also received considerable attention. Previous studies have shown that given a well-fitted target GNN, the attacker can reconstruct the sensitive training graph of this model via model inversion attacks, leading to significant privacy worries for the AI service provider. We advocate that the vulnerability comes from the target GNN itself and the prior knowledge about the shared properties in real-world graphs. Inspired by this, we propose a novel model inversion attack method on HomoGNNs and HeteGNNs, namely HomoGMI and HeteGMI. Specifically, HomoGMI and HeteGMI are gradient-descent-based optimization methods that aim to maximize the cross-entropy loss on the target GNN and the $1^{st}$ and $2^{nd}$-order proximities on the reconstructed graph. Notably, to the best of our knowledge, HeteGMI is the first attempt to perform model inversion attacks on HeteGNNs. Extensive experiments on multiple benchmarks demonstrate that the proposed method can achieve better performance than the competitors.
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）
National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院多光谱信息智能处理技术国家重点实验室）；Huawei International Co. Ltd.（华为国际有限公司）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Electrical Engineering and Computer Science, Peking University（北京大学电气与计算机工程学院）
Key Laboratory for Information Science of Electromagnetic Waves, Ministry of Education, Fudan University（复旦大学电磁波信息科学教育部重点实验室）；Discipline and Technology Center of Microwave Vision Intelligent Sensing, Fudan University（复旦大学微波视觉智能感知学科与技术中心）
Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
School of Computer Science；Technology Tianjin University Tianjin China；North China University of Science；School of Economics；Management Beijing University of Posts；Departments of Health Technology \& Informatics；Computing The Hong Kong Polytechnic University Kowloon Hong Kong；Tianjin University；Beijing University of Posts；The Hong Kong Polytechnic University
A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions
School of Electrical and Computer Engineering, National Technical University of Athens, Greece（电气与计算机工程学院，国家技术大学亚历山大学院，希腊）；Department of Informatics and Telematics, Harokopio University of Athens, Greece（信息与电信学院，哈罗基奥大学亚历山大学院，希腊）
