Can graph neural networks generalize to graphs that are different from the graphs they were trained on, e.g., in size? In this work, we study this question from a theoretical perspective. While recent work established such transferability and approximation results via graph limits, e.g., via graphons, these only apply non-trivially to dense graphs. To include frequently encountered sparse graphs such as bounded-degree or power law graphs, we take a perspective of taking limits of operators derived from graphs, such as the aggregation operation that makes up GNNs. This leads to the recently introduced limit notion of graphops (Backhausz and Szegedy, 2022). We demonstrate how the operator perspective allows us to develop quantitative bounds on the distance between a finite GNN and its limit on an infinite graph, as well as the distance between the GNN on graphs of different sizes that share structural properties, under a regularity assumption verified for various graph sequences. Our results hold for dense and sparse graphs, and various notions of graph limits.
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications（重庆邮电大学网络安全与信息法学院）；School of Software Engineering, Chengdu University of Information Technology（成都信息科技学院软件工程学院）；School of Computer and Information Science, Chongqing Normal University（重庆师范大学计算机与信息科学学院）；School of Computer Science, University of Technology Sydney（悉尼大学计算机科学学院）
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Emory University（埃默里大学）；University of Washington（华盛顿大学）；Georgia Institute of Technology（佐治亚理工学院）
Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs
Department of Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）；Halıcıoğlu Data Science Institute, UCSD（加州大学圣地亚哥分校Halıcıoğlu数据科学研究所）
Sampling Transferable Graph Neural Networks with Limited Graph Information
Department of Applied Mathematics and Statistics, Johns Hopkins University（应用数学与统计学系，约翰霍普金斯大学）；Department of Statistics and Data Science, Yale University（统计与数据科学系，耶鲁大学）；Department of Electrical and Computer Engineering, University of Rochester（电气与计算机工程系，罗切斯特大学）
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks
School of Cyber Science and Engineering, Sichuan University（四川大学网络空间安全学院）；School of Cyber Science and Engineering, Chengdu University of Information Technology（成都信息工程大学网络空间安全学院）；School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间安全学院）
Model-to-Data Distillation for Graph Neural Networks
Department of Computer Science, Rice University, Houston, TX 77005, USA（计算机科学系，里士大学，休斯顿，德克萨斯州，77005，美国）；Department of Computer Science & Ken Kennedy Institute, Rice University, Houston, TX 77005, USA（计算机科学系及肯尼迪学院，里士大学，休斯顿，德克萨斯州，77005，美国）
PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs
Peng Cheng Laboratory（鹏城实验室）；The University of Auckland（奥克兰大学）；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology（香港科技大学）；Peking University（北京大学）
Tencent AI Lab（腾讯人工智能实验室）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Rutgers University（罗格斯大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；University of Science and Technology of China（中国科学技术大学）；Meta AI；North Carolina State University（北卡罗来纳州立大学）
