Generalizing GNNs with Tokenized Mixture of Experts
University of Connecticut Storrs（康涅狄格大学斯特劳斯分校）；University of Notre Dame（Notre Dame 大学）；University of Virginia（弗吉尼亚大学）；Northwestern University Evanston（北western 大学埃文斯顿分校）
Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations. We show that static inference induces a fundamental tradeoff: improving stability requires reducing reliance on shift-sensitive features, leaving an irreducible worst-case generalization floor. Instance-conditional routing can break this ceiling, but is fragile because shifts can mislead routing and perturbations can make routing fluctuate. We capture these effects via two decompositions separating coverage vs selection, and base sensitivity vs fluctuation amplification. Based on these insights, we propose STEM-GNN, a pretrain-then-finetune framework with a mixture-of-experts encoder for diverse computation paths, a vector-quantized token interface to stabilize encoder-to-head signals, and a Lipschitz-regularized head to bound output amplification. Across nine node, link, and graph benchmarks, STEM-GNN achieves a stronger three-way balance, improving robustness to degree/homophily shifts and to feature/edge corruptions while remaining competitive on clean graphs.
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
Institute of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Hopcroft Center on Computing Science, Huazhong University of Science and Technology（华中科技大学霍普克罗夫特计算科学中心）
Theoretical guarantees for the advantage of GNNs over NNs in generalizing bandlimited functions on Euclidean cubes
Faculty of Mathematics, University of Vienna（维也纳大学数学学院）；Department of Mathematics, Michigan State University（密歇根州立大学数学系）；Department of Statistics and Probability, Michigan State University（密歇根州立大学统计与概率系）
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
The School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；The Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Tencent AI Lab（腾讯AI实验室）
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
School of Computer Science and Engineering（计算机科学与工程学院）；University of Electronic Science and Technology of China（电子科技大学）；th Research Institute of CETC（中国电子科技集团公司第五十四研究所）
Recurrent Graph Neural Networks and Arithmetic Circuits
Institut für Theoretische Informatik, Leibniz Universität Hannover, Germany（汉诺威莱布尼茨大学理论计算机研究所）；School of Computing Science, University of Glasgow, UK（格拉斯哥大学计算机科学学院）
Generalized Laplacian Positional Encoding for Graph Representation Learning
Ludwig-Maximilian University of Munich（慕尼黑大学）；MILA（蒙特利尔学习算法研究所）；New Jersey Institute of Technology（新泽西理工学院）；TU Wien（维也纳工业大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Vienna（维也纳大学）；NVIDIA Research（英伟达研究院）
Weizmann Institute of Science（魏茨曼科学研究所）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；NVIDIA Research（英伟达研究院）；Meta AI Research Centre for Artificial Intelligence（Meta人工智能研究中心）
Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Kyoto University（京都大学）；Ateneo de Manila University（马尼拉雅典耀大学）；UNI-President Information Philippines Corporation（统一信息菲律宾公司）；The Chinese University of Hong Kong（香港中文大学）
Plain Transformers are Surprisingly Powerful Link Predictors
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.（计算机科学与工程系，密歇根州立大学，东兰辛，MI，美国。）；Department of Computer Science（计算机科学系）；Engineering, Michigan State University, East Lansing, MI, USA.（工程，密歇根州立大学，东兰辛，MI，美国。）
RWTH Aachen University（亚琛工业大学）；Department of Physics, RWTH Aachen University（物理系，亚琛工业大学）；Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience, Jülich Research Centre（先进模拟研究所（IAS-6），计算与系统神经科学，耶鲁研究中心）
View Space: Learning Representation across Arbitrary Graphs
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea（韩国釜山科学技术院电子工程学院）；Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea（首尔国立大学计算机科学与工程系）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Agricultural Information Institute, China Academy of Agricultural Sciences（中国农业科学院农业信息研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Peng Cheng Laboratory（鹏城实验室）
