Graph Neural Networks

Also known as GNN

Graph Neural Networks (GNNs) study how to learn from data whose entities and relationships form a graph, supporting tasks such as node classification, link prediction, and graph or molecular property prediction. Common approaches include message-passing networks such as GCN, GraphSAGE, and GIN, attention-based Graph Transformers, and spectral, geometric, or higher-order methods that encode richer structure. They are used on benchmarks such as Cora and ogbn-arxiv, as well as in molecular modeling, recommender systems, scientific simulation, and network control. Key open challenges include handling heterophilic graphs, preserving useful information in deep networks without oversmoothing or oversquashing, and achieving reliable generalization, robustness, and efficiency on large or shifting graphs.

Latest papers 686

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

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Learning Subgroup Relations Using Siamese Graph Neural Networks
Jul 13, 2026Tal WeissblatGraph Neural Networks Graph Representation Learning
A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning
Jul 13, 2026Junpeng Wu, Ye YuanGraph Neural Networks Graph-Based Semi-Supervised Learning
College of Computer and Information Science Southwest University Chongqing, China
GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation
Jul 13, 2026Yeonseo Lee, Taeyeop Lee, Hyosup Shin +2Robotic Grasping Graph Neural Networks
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
When does distribution shift break graph neural networks calibration?
Jul 12, 2026Abderaouf BahiGraph Neural Networks Model Calibration
Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University, El Tarf 36000, Algeria
SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention
Jul 11, 2026Lidia Losavio, Francesco Sovrano, Dario Fenoglio +2Graph Attention Networks Graph Neural Networks
Università della Svizzera italiana (USI) Lugano, Switzerland
Distance-Preserving Embeddings in Inhomogeneous Random Graphs
Jul 11, 2026My Le, Luana Ruiz, Souvik DharaGraph Neural Networks Graph Embedding
Department of Applied Mathematics & Statistics Johns Hopkins University Baltimore, MD 21218, USA · School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332, USA
Graph Neural Networks for Scalable and Transferable Node Centrality Approximation
Jul 10, 2026Samra Sana, Giorgio Mantica, Saul ImbriciGraph Neural Networks Domain Generalization
Center for Nonlinear and Complex Systems, Università degli Studi dell’Insubria, Como, Italy
Power Flow Feasibility Assessment Using Variational Graph Autoencoders
Jul 10, 2026Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo +2Graph Neural Networks Power Systems
CITCEA UPC Barcelona, Spain · BNN UPC Barcelona,Spain
Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification
Jul 10, 2026Camila Piscioneri Magalhães, Lucas Pascotti ValemGraph Neural Networks Graph-Based Semi-Supervised Learning
Institute of Mathematics and Computer Science (ICMC) University of São Paulo (USP) São Carlos – SP – Brazil
PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation
Jul 10, 2026Devanshu Verma, Vasudha Bhatnagar, Vikas Kumar +1Graph Neural Networks Legal IR
aUniversity of Delhi, Delhi, India · bIBM Research, Bengaluru, India
Model Agnostic Graph Prompt Learning for Crystal Property Prediction
Jul 9, 2026Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2Graph Neural Networks Prompt Learning
School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science, Kolkata, India · Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India
Pattern-Aware Graph Neural Networks for Handling Missing Data
Jul 9, 2026Minett Tran, Taehee JeongGraph Neural Networks Learning with Missing Data
San Jose State University
EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy
Jul 9, 2026Wenxiu Ding, Muzhi Liu, Zheng Yan +3Graph Neural Networks Privacy-Preserving ML
State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University, Xi’an, Shaanxi, China · School of Cyber Engineering, Xidian University, Xi’an, Shaanxi, China
A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals
Jul 8, 2026Pragatheeswaran Vipulanandan, Kamal Premaratne, Manohar MurthiEMG-Based Gesture Recognition Graph Neural Networks
Department of Electrical and Computer Engineering University of Miami Coral Gables, Florida, USA
Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions
Jul 8, 2026Faranak Hatami, Mousa MoradiClass-Imbalanced Learning Graph Neural Networks
Department of Chemistry, University of Illinois Chicago, Chicago, IL 60607, USA · Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
MVMGNN;Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI
Jul 8, 2026Ni Yao, Zhenxu Wang, Danyang Sun +6Graph Neural Networks Multi-View Learning
School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China · Department of Computer Science, Kennesaw State University Marietta, GA, USA · Department of Applied Computing, Michigan Technological University, Houghton, MI, USA +1
Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraints
Jul 8, 2026Alan Gany, Bogdan Cautis, Silviu ManiuGraph Neural Networks Adversarial Attacks
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG Grenoble, France
Generative Diffusion Models of Stochastic Graph Signals
Jul 7, 2026Yiğit Berkay Uslu, Samar Hadou, Sergio Rozada +2Graph Neural Networks Financial Forecasting
Dept. of Electrical and Systems Eng., University of Pennsylvania, Philadelphia, PA 19104 USA · Department of Signal Processing and Communications, King Juan Carlos University, Madrid, Spain
Hardware-aware Graph Neural Networks prunning for embedded event-based vision
Jul 7, 2026Piotr Wzorek, Kamil Jeziorek, Tomasz KryjakEvent-Based Vision Graph Neural Networks
Embedded Perception and Autonomous Systems Group, AGH University of Krakow, Poland
Visual graphs for image classification: does the structure affect performance?
Jul 7, 2026Alessandra IbbaGraph Structure Learning Graph Neural Networks
University of Sassari - Department of Engineering, Sassari, Italy
FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training
Jul 6, 2026Yushu Cai, Qingrui Zhu, Lei Liu +3Temporal GNNs Graph Neural Networks
Xidian University Guangzhou, Guangdong, China · Hunan University Changsha, Hunan, China
Hyperparameter Transfer in Graph Neural Networks
Jul 6, 2026Gage DeZoort, Boris HaninGraph Neural Networks Neural Network Optimization
Department of Operations Research and Financial Engineering · School of Engineering and Applied Science · Princeton University +1
Towards Personalized Differentially Private Learning for Decentralized Local Graphs
Jul 6, 2026Longzhu He, Peng Tang, Chaozhuo Li +5Graph Neural Networks Decentralized Learning
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China · State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China · School of Cyber Science and Technology, Shandong University, Qingdao, China +2
On Preserving Geometrical Invariance for Superpixel Image Classification using Graph Transformer
Jul 5, 2026Sarabeshwar Balaji, Shubham Mohanty, Akash AnilGraph Neural Networks Invariant Representation Learning
Indian Institute of Science Education and Research Bhopal, Bhopal, Madhya Pradesh, 462066, India
Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks
Jul 5, 2026Yi Lan, Ye YuanGraph Neural Networks Reinforcement Learning
College of Computer and Information Science, Southwest University, Tiansheng Road, Chongqing, 400715, China
SGF-CDNet: A Consistency-Discrepancy Graph Network over Semantic-Geometric Fused Nodes for Face Forgery Detection
Jul 4, 2026Jiayao Jiang, Bin Liu, Nenghai YuGraph Neural Networks Image Forgery Detection
School of Cyber Science and Technology, University of Science and Technology of China, China · Anhui Province Key Laboratory of Digital Security, China
Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks
Jul 4, 2026Yoshihiro MaruyamaGraph Neural Networks Equivariant Neural Networks
Department of Mathematical and Information Sciences, Kyoto University, Kyoto, Japan.
Graph Neural Networks for the Graphical Bootstrap
Jul 3, 2026Rigers Aliaj, Gabriele Dian, Reza Doobary +1Graph Neural Networks High-Energy Physics
Deutsches Elektronen-Synchrotron DESY Hamburg, Germany · Independent Researcher United Kingdom · Department of Mathematical Sciences, Durham University Durham, United Kingdom
Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN
Jul 3, 2026Long Zhao, Shixun Ji, Bin Cheng +1Graph Neural Networks IoT Intrusion Detection
Department of Control Science & Engineering, Tongji University, Shanghai 201804, China · National Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai 201203, China
SA-HGNN: Sample-Adaptive Hyperbolic Graph Neural Network for EEG-Based Depression Recognition
Jul 2, 2026Yang Li, Pan Hu, Yan Zhang +3Graph Structure Learning Electroencephalography
School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China

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Learning Subgroup Relations Using Siamese Graph Neural Networks

Tal Weissblat

Graph Neural Networks Graph Representation Learning

Open paper AskPDF arXiv

Determining whether one finite group is isomorphic to a subgroup of another is a fundamental problem in computational group theory. In this work, we propose a Siamese Graph Neural Network (Siamese GNN) for subgroup prediction using Cayley graph representations of finite groups. Each input group is represented by its undirected Cayley graph and encoded by one branch of a Siamese GNN to produce a graph embedding. The resulting graph embeddings are combined with algebraic features derived directly from the input groups to construct a joint feature vector, which is processed by a fully connected classifier to predict subgroup relations between finite groups. By integrating graph-based structural representations with algebraic features, the proposed framework provides a unified approach for learning subgroup relations from finite groups. Experimental results demonstrate the effectiveness of the proposed architecture, achieving a test accuracy of 95.9% (47/49) on an independent test set and illustrating the potential of geometric deep learning for subgroup prediction.