Graph Neural Networks

Also known as GNN

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88 papers in the last four weeks, up 31% on the four weeks before. 0.5% of all new papers.

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The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Jun 4, 2026Shuo Wang, Xiangyu Wang, Quanxin Wang +9Graph Neural Networks Graph Representation Learning
University of Electronic Science and Technology of China · Tsinghua University · Western University +1
AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling
Jun 4, 2026Gabriela Dobrita, Simona-Vasilica Oprea, Adela BaraGraph Neural Networks Software Vulnerability Detection
Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, no.6 Piața Romană, Bucharest, 010374, Romania
Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion
Jun 4, 2026Dongxiao He, Ruqiong Zhang, Zhizhi Yu +4Graph Neural Networks KG Completion
College of Intelligence and Computing, Tianjin University
Graph Set Transformer
Jun 3, 2026Jose E. Escrig Molina, Baoquan Chen, Daniel ProbstGraph Neural Networks Graph Representation Learning
Bioinformatics Group Wageningen University Droevendaalsesteeg 6708 PB Wageningen The Netherlands · Department of Physics Technical University of Munich 85748 Garching Germany
Treat Traffic Like Trees: A Semantic-Preserving Hierarchical Graph-Based Expert Framework for Encrypted Traffic Analysis
Jun 3, 2026Yuantu Luo, Jun Tao, Linxiao Yu +1Graph Neural Networks Encrypted Network Traffic Analysis
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China · Purple Mountain Laboratories, Nanjing 210096, China · Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University), MoE, Nanjing 211189, Jiangsu, China +1
Contrastive Neural Algorithmic Reasoning for Graph Coloring
Jun 2, 2026Thien Le, Tianyu Zhao, Melanie WeberGraph Neural Networks Graph Representation Learning
Harvard University SEAS · Harvard University T.H. Chan School of Public Health
Text-attributed Graph Condensation via Text Selection and Attribute Matching
Jun 2, 2026Haowei Han, Yuxiang Wang, Guojia Wan +5Graph Neural Networks Text-Attributed Graph Learning
School of Computer Science Wuhan University Wuhan, China · Institute for Math & AI Wuhan University Wuhan, China
Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs
Jun 2, 2026Romina Garcia Camargo, Zhiyang Wang, Alejandro RibeiroGraph Neural Networks Wireless Resource Allocation
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA · Halıcıoğlu Data Science Institute, UCSD, La Jolla, CA
Bayesian Membership Privacy for Graph Neural Networks
Jun 2, 2026Sinan Yıldırım, Megha KhoslaPrivacy Auditing Graph Neural Networks
Faculty of Engineering and Natural Sciences Sabancı University Istanbul, Turkey · Intelligent Systems Department Delft University of Technology (TU Delft) Delft, The Netherlands
Topology-Aware Gaussian Graph Repair for Robust Graph Neural Networks
Jun 2, 2026Anubha Goel, Juho KanniainenGraph Structure Learning Graph Neural Networks
Computing Science/Financial Computing and Data Analytics Group, Tampere University, Tampere, 33720, Finland
Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification
Jun 2, 2026Jaeyoon Sim, Minjae Lee, Guorong Wu +1Graph Neural Networks Alzheimer's Disease Classification
Pohang University of Science and Technology, Pohang, South Korea · University of North Carolina at Chapel Hill, Chapel Hill, USA
A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation
Jun 2, 2026Ankang Yang, Jitao Zhao, Dongxiao He +3Graph Neural Networks Cross-Domain Generalization
School of Computer Science and Technology Tianjin University Tianjin, China · School of Artificial Intelligence Hebei University of Technology Tianjin, China · Department of Health Technology and Informatics Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Kowloon, Hong Kong
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
Jun 2, 2026Parth Verma, Parv P. Singh, Vipul Garg +3Graph Neural Networks Machine Learning Interatomic Potentials
Department of Computer Science, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India · Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India · Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs
Jun 2, 2026Jaejun Lee, Joyce Jiyoung WhangGraph Structure Learning Graph Neural Networks
School of Computing KAIST Daejeon, Republic of Korea · Department of AI Computing KAIST Daejeon, Republic of Korea
Uncertainty-Aware Graph Neural Reconstruction of Urban Temperature Fields from Sparse Sensors under Deployment Constraints
Jun 1, 2026Reda Snaiki, Abdelatif MerabtineGraph Neural Networks Uncertainty Quantification
Department of Construction Engineering, École de Technologie Supérieure, Université du Québec, Montréal, Québec, Canada
Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks
Jun 1, 2026Bryan Cheng, Austin JinGraph Neural Networks Molecular Property Prediction
Independent Researcher USA
Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs
Jun 1, 2026Zichao Yue, Zhiru ZhangGraph Neural Networks Mixture of Experts
School of Electrical and Computer Engineering, Cornell University Ithaca, New York, USA
GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
Jun 1, 2026Canyixing Cui, Tao Wu, Xingping Xian +3Disentangled Representation Learning Graph Neural Networks
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China · School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, China · Computational Communication Research Center, Beijing Normal University, Zhuhai, China +2
Learning-based Directed Graph Abstraction of Combinatorial Spaces for Order-Preserving Search in Mixed-Combinatorial Nonlinear Optimization
May 31, 2026Gishnu Madhu, Feng Liu, Souma ChowdhuryGraph Structure Learning Graph Neural Networks
University at Buffalo Buffalo, New York, 14260
RADE: Random Add-Drop Edge as a Regularizer
May 30, 2026Danial Saber, Amirali Salehi-AbariNeural Network Generalization Graph Neural Networks
Ontario Tech University, Oshawa, Ontario, Canada.
Graph Neural Networks for Predicting Solvability of Finite Groups
May 30, 2026Tal WeissblatGraph Neural Networks
Scaling Higher-Order Graph Learning with Maximal Clique Complexes
May 29, 2026Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros +1Graph Neural Networks Graph Representation Learning
LTCI, Télécom Paris · Institut Polytechnique de Paris · Palaiseau, France +4
Graph Neural Networks Are Not Continuous Across Graph Resolutions
May 29, 2026Christian Koke, Yuesong Shen, Abhishek Saroha +4Neural Network Generalization Graph Neural Networks
AITHYRA · Munich Center for Machine Learning · Technical University of Munich, Munich +3
An Efficient and Scalable Graph Condensation with Structure-Preserving
May 29, 2026Yulin Hu, Fuyan Ou, Ye YuanGraph Neural Networks
Southwest University Chongqing, China
Physics-Informed Coarsening for Multigrid Graph Neural Surrogates
May 29, 2026Amir Bazzi, David Cardinaux, Ramy Nemer +3Graph Neural Networks Neural PDE Solvers
1CEMEF, Mines Paris – PSL, Sophia Antipolis, France · 2Aubert et Duval, France · 3Transvalor, Mougins, France.
A Topological Characterization of Graph Neural Networks via Stochastic Block Model Embeddings on the n-Sphere
May 29, 2026Gopal AnantharamanGraph Neural Networks Neural Representation Geometry
KnotTheory.ai Inc. · Dept. of Mathematics, Emporia State University
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
May 29, 2026Xixun Lin, Zhiheng Zhou, Zhengyin Zhang +9Graph Neural Networks Selective Prediction
Institute of Information Engineering, Chinese Academy of Sciences · School of Mathematics and Statistics, Shandong University · China University of Mining and Technology-Beijing +6
Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
May 28, 2026Ojas Nimase, Jiate Li, Yue Zhao +1Graph Neural Networks Model Extraction Attacks
University of Southern California Los Angeles, USA · Florida State University Tallahassee, USA
SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection
May 28, 2026Hanning Lu, Yingguang Yang, Jinwei Su +8Social Media Analysis Graph Neural Networks
University of Leeds · University of Science and Technology of China · 3South China Normal University +6
Gated Graph Attention Networks with Learnable Temperature
May 28, 2026Zhongtian Ma, Hao Wu, Yexin Zhang +2Graph Attention Networks Graph Neural Networks
School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China · Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China

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The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning

Shuo Wang, Xiangyu Wang, Quanxin Wang, Bailin Wu+8

University of Electronic Science and Technology of China · Tsinghua University · Western University+1

Graph Neural Networks Graph Representation Learning Geometric Representation Learning Graph Foundation Models

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Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlying structure. We show that this assumption introduces systematic bias: it obscures geometry-dependent performance variations and can lead to misleading conclusions about model generalization. In this work, we identify intrinsic geometry as a key latent factor governing model effectiveness. We demonstrate that conventional aggregated metrics mask critical performance trade-offs that only become visible when datasets are stratified by their geometric properties. To address this issue, we introduce a curvature-stratified evaluation framework that partitions datasets into positive, negative, and near-zero curvature regimes. Our benchmark evaluates 18 representative models including Graph Convolutional Networks (GCNs), Graph Foundation Models (GFMs), and tabular learning methods across 14 datasets. We find that model rankings are highly stable within each curvature regime but shift significantly across regimes, indicating that performance is fundamentally geometry-dependent rather than universally transferable. Notably, we identify regimes where GFMs offer diminishing returns compared to geometry-aligned GNNs. Based on these findings, we propose a geometry-aware evaluation protocol that yields more reliable and interpretable comparisons than standard aggregated benchmarks. We release all code, curvature-stratified dataset splits, and evaluation tools to support reproducible and rigorous assessment of future relational learning methods. Code and datasets are provided in our project homepage: https://sirbabbage.github.io/CurvBench_HOME/.