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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No Triangulation Without Representation: Generalization in Topological Deep Learning
May 7, 2026Johannes S. Schmidt, Martin Carrasco, Ernst Röell +3Neural Network Generalization Graph Neural Networks
Dept. of Math. & Statistics University of Montréal Montréal, Canada · Dept. of Informatics University of Fribourg Fribourg, Switzerland
Measuring Structure in Graph Benchmark Datasets Using Graph Invariants
May 7, 2026Richard von Moos, Mathieu Alain, Bastian RieckGraph Neural Networks Graph Representation Learning
University of Fribourg · University College London
Federated Layer-Wise Exchange for Cross-Client Money Laundering Pattern Detection
May 7, 2026Selin Ceydeli, Rui Wang, Kubilay AtasuGraph Neural Networks Federated Graph Learning
Delft University of Technology, Department of Software Technology Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
May 7, 2026Kartik Tandon, Julian Gould, Tanishq Bhatia +3Graph Neural Networks Geometric Representation Learning
University of Pennsylvania · Sakana AI · Northeastern University +1
The Role of Node Features in Graph Pooling
May 7, 2026Jan von Pichowski, Alžbeta Hrabošová, Ingo Scholtes +1Graph Neural Networks Graph Pooling
Center for AI and Data Science University of Würzburg, Germany · Department of Computing Science Umeå University, Sweden
Full-Spectrum Graph Neural Networks: Expressive and Scalable
May 7, 2026Xiaohan Wang, Deyu Bo, Longlong Li +1Graph Neural Networks GNN Expressivity
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.
WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
May 7, 2026Dhruv Suri, Helgi Hilmarsson, Shourya BosePrimal-Dual Optimization Graph Neural Networks
Pravah
Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
May 7, 2026Tran Gia Bao Ngo, Zulfikar Alom, Federico Errica +2Graph Neural Networks Adversarial Attacks
Department of Computer Science, University of Manitoba · University of Toledo · NEC Laboratories Europe +2
A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
May 6, 2026Othmane Kabal, Mounira Harzallah, Fabrice Guillet +2KG Construction Graph Neural Networks
Nantes University, LS2N, Nantes, 44300, France · National Institute of Informatics, Chiyoda-ku, Tokyo, 101-8430, Japan · Institute of Science Tokyo, Tokyo, 152-8550, Japan
COPYCOP: Ownership Verification for Graph Neural Networks
May 6, 2026Rahul Nandakumar, Deepayan ChakrabartiGraph Neural Networks
McCombs School of Business University of Texas at Austin Austin, TX
Quantile-Free Uncertainty Quantification in Graph Neural Networks
May 6, 2026Soyoung park, Hwanjun Song, Sungsu LimGraph Neural Networks Uncertainty Quantification
Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea · Department of Industrial and Systems Engineering, KAIST, Daejeon, Korea
Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
May 5, 2026Ali Rayat, Gia-Wei ChernNeural Surrogate Modeling Graph Neural Networks
Department of Physics, University of Virginia, Charlottesville, VA 22904, USA
Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
May 5, 2026Filip Kronström, Alexander H. Gower, Daniel Brunnsåker +2Graph Neural Networks Representation Learning
Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Sweden · Department of Life Sciences, Chalmers University of Technology, Sweden · Department of Industrial Biotechnology, KTH Royal Institute of Technology, Sweden +1
GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
May 5, 2026Rishi Raj Sahoo, Subhankar MishraFeature Attribution Graph Neural Networks
National Institute of Science Education and Research (NISER), Bhubaneswar, India · Homi Bhabha National Institute, Mumbai, India
DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
May 4, 2026Danil Tokhchukov, Veronika Morozova, Gonzalo FerrerSimultaneous Localization and Mapping Pedestrian Behavior Modeling
Applied AI Institute, Moscow, Russia.
SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing
May 4, 2026Genhao Tian, Taihua Xu, Shuyin Xia +3Graph Structure Learning Graph Neural Networks
JiangSu University of Science and Technoloy, Zhenjiang, Jiangsu, China · Chongqing University of Posts and Telecommunications, Chongqing, China · Zunyi Normal University, Zunyi, Guizhou, China
Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction
May 3, 2026G. Victor DanielGraph Neural Networks Privacy-Preserving ML
School of Engineering, Anurag University, Hyderabad, Telangana, India
TCDA: Thread-Constrained Discourse-Aware Modeling for Conversational Sentiment Quadruple Analysis
May 3, 2026Xinran Li, Xinze Che, Yifan Lyu +2Rotary Positional Embeddings Graph Neural Networks
School of Software, Dalian University of Technology, Dalian, China · School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
Mesh Based Simulations with Spatial and Temporal awareness
May 2, 2026Paul Garnier, Vincent Lannelongue, Elie HachemGraph Neural Networks Neural PDE Solvers
1CEMEF - Mines Paris PSL
Rethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?
May 2, 2026Yuxuan Xiao, Shengzhong ZhangMulti-Label Classification Graph Neural Networks
Nanjing University of Aeronautics and Astronautics
Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
May 1, 2026Sanjukta KrishnagopalGraph Neural Networks Neural Representation Geometry
Department of Computer Science, University of California Santa Barbara
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
May 1, 2026Hugo Attali, Nathalie Pernelle, Davide Buscaldi +1Graph Neural Networks Graph Rewiring
Universit´e Sorbonne Paris Nord, CNRS, LIPN · Universit´e Paris-Saclay, CentraleSup´elec, Inria
On the Expressive Power of GNNs to Solve Linear SDPs
Apr 30, 2026Chendi Qian, Christopher MorrisSemidefinite Programming Graph Neural Networks
Faculty of Computer Science, RWTH Aachen University, Germany
Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
Apr 30, 2026Sivaram Krishnan, Bassel Al Homssi, Zhouyou Gu +3Graph Neural Networks Wireless Communications
School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide 5000, Australia
Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation
Apr 29, 2026Yanchen Guan, Haicheng Liao, Chengyue Wang +4Autonomous Driving Datasets Synthetic Data Augmentation
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau SAR, 999078, China. · Department of Civil Engineering, University of Macau, Macau SAR, 999078, China. · Department of Computer and Information Science, University of Macau, Macau SAR, 999078, China. +1
ModTGCN: Modularity-aware Graph Neural Networks for Text Classification
Apr 29, 2026Rajarshi Misra, Aditya Sharma, Vinti Agarwal +1Graph Neural Networks Text Classification
BITS Pilani, India · Independent Researcher
Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
Apr 29, 2026Abdul Joseph Fofanah, Lian Wen, David Chen +2Graph Neural Networks Spiking Neural Networks
School of ICT, Griffith University, Australia
Momentum-Conserving Graph Neural Networks for Deformable Objects
Apr 28, 2026Jiahong Wang, Logan Numerow, Stelian Coros +3Graph Neural Networks Physics-Informed ML
Max Planck Institute for Informatics · ETH Zurich · University of Bonn +1
PLMGH: What Matters in PLM-GNN Hybrids for Code Classification and Vulnerability Detection
Apr 28, 2026Mohamed Taoufik Kaouthar El Idrissi, Edward Zulkoski, Mohammad HamdaqaGraph Neural Networks Software Vulnerability Detection
Polytechnique Montréal Montréal, Canada · Quantstamp San Francisco, United States
On Halting vs Converging in Recurrent Graph Neural Networks
Apr 28, 2026Jeroen Bollen, Stijn VansummerenGraph Neural Networks GNN Expressivity
Hasselt University

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No Triangulation Without Representation: Generalization in Topological Deep Learning

Johannes S. Schmidt, Martin Carrasco, Ernst Röell, Guy Wolf+2

Dept. of Math. & Statistics University of Montréal Montréal, Canada · Dept. of Informatics University of Fribourg Fribourg, Switzerland

Neural Network Generalization Graph Neural Networks Message Passing Neural Networks Topological Representation Learning Topological Data Analysis

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Despite an ever-increasing interest in topological deep learning models that target higher-order datasets, there is no consensus on how to evaluate such models. This is exacerbated by the fact that topological objects permit operations, such as structural refinements, that are not appropriate for graph data. In this work, we extend MANTRA, a benchmark dataset containing manifold triangulations, to a larger class of manifolds with more diverse homeomorphism types. We show that, unlike prior claims, both graph neural networks (GNNs) and higher-order message passing (HOMP) methods can saturate the benchmark. However, we find that this is contingent on the right representation and feature assignment, emphasizing their importance in baseline models. We thus provide a novel evaluation protocol based on representational diversity and triangulation refinement. Surprisingly, we find no indication that existing models are capable of generalizing beyond the combinatorial structure of the data. This points towards a research gap in developing models that understand topological structure independent of scale. Our work thus provides the necessary scaffolding to evaluate future models and enable the development of topology-aware inductive biases.