Message Passing Neural Networks

Also known as MPNN

Message passing neural networks study how to learn from graph-structured data by repeatedly sending, aggregating, and transforming information between connected nodes. Researchers vary the aggregation and update rules, incorporate edge and geometric features, and add mechanisms such as graph rewiring, virtual nodes, or global mixing to carry information farther. These models are used for node and graph prediction, molecular property estimation, recommendation, combinatorial optimization, and scientific simulation. A central tension is that local propagation is efficient and structure-aware but can lose long-range information or become indistinguishable across graphs; improving reach and expressivity without sacrificing efficiency or stability remains an open challenge.

Latest papers

64 papers8 papers in the last four weeks, up 167% on the four weeks before. 0.0% of all new papers.

Structure-Aware Message Passing on Petri Nets through Directed Walks and Incidence Diffusion
Oct 21, 2026Geri SkenderiRepresentation Learning Message Passing Neural Networks
HASH
SU(2)-Equivariant Spinor Message Passing for Molecular Magnetism
Oct 21, 2026Erh-Wei Sheng, Beom Seok Kang, Yuchao Lin +2Equivariant GNNs Molecular Property Prediction
Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA · Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan · Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
Degree Bounds for Separating Polynomial and Neural Invariants for Point Clouds
Oct 19, 2026Snir Hordan, Harm Derksen, Mircea Petrache +1Point Cloud Learning Equivariant Neural Networks
Technion – Israel Institute of Technology · Northeastern University · Pontificia Universidad Católica de Chile
Tiny Recursive Message Passing
Oct 13, 2026Tommaso Salvatori, Matteo Forasassi, Alexis Toumi +1Neural Algorithmic Reasoning Message Passing Neural Networks
Relational Intelligence, Inc · Vienna University of Technology
PANDA: Prior-guided Attentional Dual-path Architecture
Oct 9, 2026Rishabh Jain, Federico Belotti, Stefano Coniglio +2Hypergraph Neural Networks Higher-Order GNNs
University of Cambridge, UK · University of Bergamo, Italy · Independent Researcher
The Expressive Power of Random-Feature MPNNs Across Graph Sizes
Oct 8, 2026Robert R. Nerem, Mitchell Black, Antonis Vasileiou +2GNN Expressivity Message Passing Neural Networks
University of California, San Diego · Rheinisch Westfälische Technische Hochschule Aachen
Message Passing Does More with Less for In-Context Learning on Graphs
Sep 29, 2026Dooho Lee, Jinmo Lee, Minho Jeong +2In-Context Learning Graph Neural Networks
Nums AI · KAIST · Seoul National University
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
Reachability is not enough: Diagnosing long-range behavior in GNNs
Sep 27, 2026Filippo Maria BianchiGraph Neural Networks GNN Expressivity
UiT The Arctic University of Norway · NORCE Norwegian Research Centre
Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks
Sep 6, 2026Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe +1Over-Smoothing in GNNs Graph Neural Networks
Department of Mechanical Engineering University of Melbourne Melbourne, Australia · School of Computing Australian National University Canberra, Australia · Data61, CSIRO Canberra, Australia
Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials
Sep 1, 2026Pingbing Ming, Han WangMachine Learning Interatomic Potentials GNN Expressivity
SKLMS, Institute of Computational Mathematics and Scientific/Engineering Computing, AMSS, Chinese Academy of Sciences, No. 55, East Road Zhong-Guan-Cun, Beijing 100190, China · National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, P.R. China, and HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P.R. China
Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning
Aug 27, 2026Jintang Li, Yuhong Chen, Ruofan Wu +4Graph Neural Networks Text-Attributed Graph Learning
School of Computer Science and Engineering of Central South University, Changsha 410000, China
Read, Write, Relax: Why Neural PDE Surrogates Need Both Global and Local Processing
Aug 21, 2026Anuj Kumar, Heiko Zimmermann, Josiah Bjorgaard +4PDE Surrogate Modeling Neural PDE Solvers
Pasteur Labs, Brooklyn, NY, USA · Cornell University, Ithaca, NY, USA · Institute for Simulation Intelligence, New York, NY, USA
Unifying Graph Neural Networks Through a Common Layer Equation
Aug 17, 2026Sai Karthik Navuluru, Siddhartha Shankar Das, Bo Ni +9Graph Neural Networks Message Passing Neural Networks
University of Texas at Dallas · Pacific Northwest National Laboratory · Vanderbilt University +4
Pair-Centric Graph Rewiring for Over-Squashing via Optimal Transport-Guided Communication Alignment
Aug 11, 2026Yan Wang, Chuan-Xian RenGraph Rewiring Over-Squashing
School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
Designing a Good Virtual Node: Addressable and Cardinality-Preserving Global Memory for Message Passing Architectures
Aug 3, 2026Félix MarcocciaCross-Attention GNN Expressivity
Benchmarking Sheaf Neural Networks for Inductive Tasks
Aug 3, 2026Stefano Fiorini, Edoardo Coppola, Pietro LiòGraph Neural Networks Graph Representation Learning
1Independent Researcher · Department of Computer Science and Technology, University of Cambridge
CoRe-GNN: Multilevel Message passing on Coarsened graphs
Aug 3, 2026Antonin Joly, Nicolas Keriven, Aline RoumyGraph Neural Networks Graph Coarsening
CNRS, IRISA, Rennes, FRANCE · INRIA, Rennes, FRANCE
Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models
Jul 31, 2026Yi Wang, Jitao Zhao, Di Jin +1Message Passing Neural Networks Cross-Domain Generalization
Tianjin University Tianjin, China
Universality and Approximation Rates of Graph Neural Networks with Random Features
Jul 29, 2026Lukas Gonon, Thilo Meyer-Brandis, Niklas WeberGraph Neural Networks Equivariant Neural Networks
School of Computer Science University of St. Gallen St. Gallen, 9000, Switzerland · Department of Mathematics LMU Munich Munich, 80333, Germany
AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control
Jul 29, 2026Jingbo Cui, Jitao Zhao, Di Jin +1Graph Neural Networks Message Passing Neural Networks
School of Computer Science and Technology, Tianjin University Tianjin, China
How Much Does Message Passing Matter? A Drop-In Study of GNN Layers for Neural Network Graph Regression
Jul 29, 2026Keith G. Mills, Aedan J. DeFrates, Joong Ho KimGraph Attention Networks Graph Neural Networks
LSU ATHENA Lab
The Value of Depth in Message Passing on Sparse Graphs: A Kesten-Stigum Dichotomy
Jul 18, 2026Aseem Raj BaranwalGraph Neural Networks Message Passing Neural Networks
MeGA-MP: Metric Graph Advection Message Passing -- A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs
Jul 6, 2026Janine Strotherm, Luca Hermes, André Artelt +1Graph Representation Learning PDE Solving
Bielefeld University
Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
Jun 25, 2026Snehal Raj, Brian Coyle, Léo Monbroussou +3GNN Expressivity Message Passing Neural Networks
1LIP6, CNRS, Sorbonne Universit´e, Paris, France · 2QC Ware, Palo Alto, USA and Paris, France · School of Informatics, University of Edinburgh, Edinburgh, UK +3
MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction
Jun 18, 2026Trung Nguyen, Duc Duy NguyenMessage Passing Neural Networks Molecular Property Prediction
The Bredesen Center, University of Tennessee, Knoxville, TN 37996, USA · Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA
Magnetic HIP-NN for spin dynamics in disordered itinerant magnets
Jun 9, 2026Supriyo Ghosh, Yunhao Fan, Sheng Zhang +2Equivariant Neural Networks Message Passing Neural Networks
Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA · Department of Chemistry, University of Chicago, Chicago, IL 60637, USA · Theoretical Division and CNLS, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
Jun 9, 2026Lianze Shan, Ningchong Wang, Jitao Zhao +2Graph SSL Message Passing Neural Networks
School of Computer Science and Technology, Tianjin University, Tianjin, China · School of Future Technology, Tianjin University, Tianjin, China.
PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
Jun 4, 2026Ziling Liang, Xinping Yi, Qingsong Wen +1PAC-Bayesian Generalization Bounds Adversarial Robustness
School of Information Science and Engineering, Southeast University, Nanjing 210096, China · Squirrel Ai Learning, Bellevue, WA, 98004 USA
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

Oct 21, 2026J/K move · Enter open · S save

Structure-Aware Message Passing on Petri Nets through Directed Walks and Incidence Diffusion

Geri Skenderi

HASH

Representation Learning Message Passing Neural Networks Process Mining Graph Positional Encoding

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A common application of the process mining discipline is to connect observed event data with Petri nets, which are bipartite, directed graphical process models with rich formal underpinnings. Recent work learns vector representations of Petri nets to retrieve process models and relate them to event logs, but leaves implicit two key aspects hidden within the net's structure: directed cycles (process loops) and invariants recorded by the net's incidence matrix. We expose both of these to message-passing graph neural networks, through a positional encoding built from return probabilities of directed place-transition walks (PNRWPE), and through same-type messages gated by diffusion kernels of the incidence matrix (DA-PNGNN). Our constructions raise macro balanced accuracy in predicting the generation factors of the 96 Process Discovery Contest 2023 models from 0.751 to 0.796 with PNRWPE, which nearly doubles accuracy on process loops, and to 0.830 with DA-PNGNN, which, without retuning, also improves net-to-tree retrieval top-1 accuracy on the self-supervised ProcRosetta benchmark by 4.1 to 5.9 points.