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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Do Better Volatility Forecasts Lead to Better Portfolios? Evidence from Graph Neural Networks
May 19, 2026Rylan WadeMultivariate Time Series Forecasting Graph Neural Networks
University of Southern California
Deep Neural Sheaf Diffusion
May 18, 2026Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria FodorGraph Neural Networks Sheaf Neural Networks
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning
May 18, 2026Michael Aichmüller, Simon Ståhlberg, Martin Funkquist +1Graph Neural Networks Tree Search
RWTH Aachen University · Linköping University
Graph Hierarchical Recurrence for Long-Range Generalization
May 18, 2026Stefano Carotti, Marco Pacini, Alessio Gravina +3Graph Neural Networks Graph Representation Learning
Department of Computer Science, University of Trento, Italy. · Fondazione Bruno Kessler, Italy. · Department of Computer Science, University of Pisa, Italy.
Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
May 18, 2026Brown Zaz, Mar Gonzàlez I Català, Ferran Hernandez Caralt +2Graph Neural Networks Graph-Based Semi-Supervised Learning
University of Cambridge
Expressive Power of Deep Homomorphism Networks over Relational Databases
May 18, 2026Moritz Schönherr, Balder ten Cate, Maurice Funk +3Logical Reasoning Graph Neural Networks
Leipzig University · University of Amsterdam · Technion & RelationalAI
Learning over Positive and Negative Edges with Contrastive Message Passing
May 18, 2026Peter Pao-Huang, Charilaos I. Kanatsoulis, Michael Bereket +1Graph Neural Networks Graph Representation Learning
Department of Computer Science Stanford University · Stanford University
GraphMAR: Geometry-Aware Graph Learning Framework for Spatially Adaptive CT Metal Artifact Reduction
May 17, 2026Zilong Li, Chenglong Ma, Yiming Lei +7Graph Neural Networks Image Restoration
Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200433, China · Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China · College of Computer Science and Technology, Qingdao University, Qingdao 266071, China +2
Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
May 17, 2026Mridul Gupta, Samyak Jain, Vansh Ramani +2Graph Neural Networks Graph Representation Learning
Yardi School of Artificial Intelligence, IIT Delhi, India · Department of Computer Science and Engineering, IIT Delhi, India · Department of Chemical Engineering, IIT Delhi, India
Universal Graph Backdoor Defense: A Feature-based Homophily Perspective
May 16, 2026Mengting Pan, Fan Li, Chen Chen +1Graph Neural Networks Backdoor Detection
The University of New South Wales Sydney, Australia · University of Wollongong Wollongong, Australia
Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
May 15, 2026Jingcheng Wu, Ratan Bahadur Thapa, Mojtaba Nayyeri +4Relational Foundation Models Tabular Foundation Models
University of Stuttgart, Stuttgart, Germany · Web and Internet Science Research Group, University of Southampton, Southampton, United Kingdom
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
May 15, 2026Louisa Cornelis, Johan Mathe, Louis Van Langendonck +2Graph Neural Networks Graph Representation Learning
UC Santa Barbara · Atmo, Inc. · Universitat Politècnica de Catalunya
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
May 14, 2026Daniel Holmberg, Joel Oskarsson, Erik Wikingsson +2Graph Neural Networks Uncertainty Quantification
University of Helsinki · ETH AI Center · Linköping University
A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming
May 14, 2026Yingxue Zhao, Haoran Li, Haosu Zhou +2Neural Surrogate Modeling Graph Neural Networks
Dyson School of Design Engineering, Imperial College London, London, UK · NVIDIA, UK
Learned Suppression for 3D Keypoint Detection with a Graph-Transformer Backbone
May 14, 2026Batuhan Arda Bekar, Can Sarı, Hüseyin Can Gülkan +13D ViTs Graph Neural Networks
Bahcesehir University Computer Engineering
KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning
May 14, 2026Yisen Gao, Jiaxin Bai, Haoyu Huang +5Graph Neural Networks In-Context Learning
Department of Computer Science and Engineering, HKUST, Hong Kong, China · Department of Computer Science and Engineering, HKBU, Hong Kong, China
AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
May 14, 2026Magdalena Proszewska, N. SiddharthGraph Neural Networks Interpretable ML
School of Informatics, University of Edinburgh
Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers
May 14, 2026Lukas Schelenz, Shobha Rajanna, Denis Gosalci +6Graph Neural Networks Multi-Agent Trajectory Prediction
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation
May 13, 2026Haoran Li, Tobias Lehrer, Yingxue Zhao +5Neural Surrogate Modeling Graph Neural Networks
Dyson School of Design Engineering, Imperial College London, London, UK · TUM School of Engineering and Design, Technical University of Munich, Munich, Germany · Faculty of Mechanical Engineering, OTH Regensburg, Regensburg, Germany +1
Graph Neural Networks with Triangle-Based Messages for the Multicut Problem
May 13, 2026Jannik Irmai, Lucas Fabian Naumann, Bjoern AndresGraph Neural Networks Combinatorial Optimization
Faculty of Computer Science, TU Dresden · Center for Scalable Data Analytics and AI, Dresden/Leipzig
Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective
May 13, 2026Peiyao Wang, Liang Bai, Xian Yang +2Neural Network Generalization Graph Neural Networks
Institute of Intelligent Information Processing Shanxi University, Taiyuan, 030006, China · Alliance Manchester Business School University of Manchester, Manchester, M13 9PL, UK · Department of Mathematics Hong Kong Baptist University, Hong Kong SAR, China
Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
May 13, 2026Eden Nagar, Ya-Wei Eileen Lin, Ron LevieGraph Neural Networks Spectral Graph Convolution
Technion - Israel Institute of Technology, Faculty of Mathematics · Technical University of Munich, School of Computation, Information and Technology · Munich Center for Machine Learning
A Neural Hierarchical-Matrix Preconditioner for Real-Time GPU Solves
May 13, 2026Carl Osborne, Minghao Guo, Crystal Owens +1Graph Neural Networks High-Performance Computing
MIT CSAIL, Cambridge, USA
MLGIB: Multi-Label Graph Information Bottleneck for Expressive and Robust Message Passing
May 13, 2026Chaokai Wu, Haofu Shi, Ningxuan Ma +2Multi-Label Classification Graph Neural Networks
What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
May 13, 2026Danny Wang, Ruihong Qiu, Zi HuangGraph Neural Networks OOD Detection
The University of Queensland, Australia.
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?
May 12, 2026Kaixiang Zhao, Bolin Shen, Yuyang Dai +2Graph Neural Networks Model Extraction Attacks
University of Notre Dame · Florida State University · University of California, Berkeley
Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization
May 12, 2026Yunfei Nie, Jiawei Wang, Ruobing Yan +3Graph Neural Networks Graph Representation Learning
Brandeis University, Waltham, USA · University of California, Los Angeles, USA · Georgetown University, Washington, D.C., USA +2
Estimating Subgraph Importance with Structural Prior Domain Knowledge
May 12, 2026Changhyun Kim, Seunghwan An, Jong-June JeonGraph Neural Networks GNN Explainability
University of Seoul, Seoul, South Korea · Incheon National University, Incheon, South Korea
Random-Set Graph Neural Networks
May 12, 2026Tommy Woodley, Shireen Kudukkil Manchingal, Matteo Tolloso +2Graph Neural Networks Uncertainty Quantification
School of Engineering, Computing and Mathematics Oxford Brookes University · Department of Computer Science University of Pisa · Oxford Brookes Institute for Artificial Intelligence, Data Analysis and Systems (AIDAS)
Crash Assessment via Mesh-Based Graph Neural Networks and Physics-Aware Attention
May 12, 2026Gabriel Curtosi, Carlos Manuel Ruiz Ruiz, Fabiola Cavaliere +1Neural Surrogate Modeling Graph Neural Networks
SEAT S.A., Barcelona, Spain · IDIADA Automotive Technology S.A., Santa Oliva, Spain

May 19, 2026J/K move · Enter open · S save

Do Better Volatility Forecasts Lead to Better Portfolios? Evidence from Graph Neural Networks

Rylan Wade

University of Southern California

Multivariate Time Series Forecasting Graph Neural Networks Quantitative Finance Time Series Forecasting Portfolio Optimization

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This paper tests whether graph neural networks improve realized volatility forecasts and whether those forecasts improve portfolio performance. Using weekly realized volatility for 465 S&P 500 equities from 2015-2025, Heterogeneous Autoregressive and Long Short-Term Memory baselines are compared against GraphSAGE models built on rolling correlation, sector, and Granger-causal graphs, with and without macro regime features. The empirical finding is that the model with the lowest forecast MSE, the model with the highest cross-sectional ranking accuracy, and the model with the highest portfolio Sharpe ratio are three different models. Forecast accuracy, ranking quality, and portfolio performance are related but not interchangeable objectives. Graph volatility models add value only when the portfolio rule can exploit the cross-sectional structure they encode.