Recurrent GNNs

GNN: Graph Neural Network

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8 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Graph-Recurrent Learning of Emergent Collective Dynamics
Oct 22, 2026Ali Rayat, Acacia Lee, Gia-Wei ChernRecurrent GNNs Graph Neural Networks
Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA
Learning Propagation Geometry from Message-Passing Feedback
Sep 28, 2026Yingxu Wang, Kunyu Zhang, Xinwang Liu +4Graph Neural Networks Message Passing Neural Networks
The Chinese University of Hong Kong · The Education University of Hong Kong · National University of Defense Technology +3
Recurrent Graph Neural Networks with Set-Based Aggregation
Sep 14, 2026Blai BonetGraph Neural Networks GNN Expressivity
Universidad Simón Bolívar Caracas, Venezuela
Finite-Time Node Separation in Recurrent Graph Neural Networks with Persistent Gaussian Perturbations
Sep 12, 2026Mostafa Haghir ChehreghaniOver-Smoothing in GNNs Recurrent GNNs
Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran, Iran
Persistent Gaussian Perturbations Prevent Oversmoothing in Recurrent Graph Neural Networks
Jul 30, 2026Mostafa Haghir ChehreghaniGraph Neural Networks Stochastic Linear Dynamical Systems
Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran, Iran
Adaptive Recurrent Message Passing for Test Time Computing on Graphs
Jun 21, 2026Junshu Sun, Wanxing Chang, Qingming Huang +1Adaptive Inference Graph Neural Networks
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · DAMO Academy, Alibaba Group, Hangzhou, China.
On Halting vs Converging in Recurrent Graph Neural Networks
Apr 28, 2026Jeroen Bollen, Stijn VansummerenGraph Neural Networks GNN Expressivity
Hasselt University
Recurrent Graph Neural Networks and Arithmetic Circuits
Mar 5, 2026Timon Barlag, Vivian Holzapfel, Laura Strieker +2Circuit Complexity Recurrent GNNs
Institut für Theoretische Informatik, Leibniz Universität Hannover, Germany · School of Computing Science, University of Glasgow, UK

Oct 22, 2026J/K move · Enter open · S save

Graph-Recurrent Learning of Emergent Collective Dynamics

Ali Rayat, Acacia Lee, Gia-Wei Chern

Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA

Recurrent GNNs Graph Neural Networks Quantum Many-Body Physics

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Collective ordering in correlated electron systems emerges from microscopic dynamics, yet the evolution of observable textures need not admit a closed, memoryless description. We introduce a graph-recurrent neural network (GRNN) framework that learns such emergent dynamics directly from trajectories without specifying microscopic equations of motion. The framework integrates gated recurrent memory with graph message passing: local hidden states encode information beyond the instantaneous observables, while spatial coupling allows this information to propagate between sites. The observable and hidden fields evolve jointly through a shared local rule, providing a scalable representation of dynamics with spatial interactions and temporal memory. We demonstrate this framework on the emergent dynamics of charge-density-wave order in the semiclassical Holstein model, successfully reproducing temporal and spatial correlations and dynamical scaling during domain coarsening. By learning finite-time evolution directly, the GRNN offers a route to efficient, data-driven descriptions of collective dynamics with unresolved microscopic degrees of freedom.