Higher-Order GNNs

GNN: Graph Neural Network

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14 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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
Higher-Order Morphology Priors for Quadruped Reinforcement Learning Under Actuator Degradation
Oct 7, 2026Derek You, Zafir Shamsi, Keqin Wang +1Higher-Order GNNs RL for Legged Locomotion
Edison Academy Magnet School. · South Brunswick High School. Work done during the Princeton Laboratory Learning Program. · Princeton University.
Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning
Oct 6, 2026Fanchen Bu, Fan Li, Geon Lee +4Benchmark Design Hypergraph Neural Networks
KAIST · UNSW Sydney · University of Oxford
Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry
Oct 1, 2026Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi +4Graph Representation Learning Molecular Generation
Department of Computing, Imperial College London, London, UK · School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK · Department of Computer Science, Stanford University, Stanford, CA, USA +1
From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks
Jul 17, 2026Zhiheng Zhou, Mengyao Zhou, Yancheng Chen +3Hypergraph Neural Networks Hypergraph Learning
School of Mathematics and Statistics, Shandong University, Weihai, Shandong 264209, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences and also with the University of Chinese Academy of Sciences, Beijing 100190, China
Scaling Higher-Order Graph Learning with Maximal Clique Complexes
May 29, 2026Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros +1Graph Neural Networks Graph Representation Learning
LTCI, Télécom Paris · Institut Polytechnique de Paris · Palaiseau, France +4
The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks
May 13, 2026Fengqing Jiang, Yuetai Li, Yichen Feng +6GNN Expressivity Hypergraph Neural Networks
University of Washington · Western Washington University · King Abdulaziz City for Science and Technology +1
Have Graph -- Will Lift? The Case for Higher-Order Benchmarks
May 8, 2026Bastian RieckBenchmark Design Higher-Order GNNs
AIDOS Lab, University of Fribourg, Switzerland · Institute of AI for Health, Helmholtz Munich, Germany
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.
Topological Neural Tangent Kernel
May 1, 2026Sanjukta KrishnagopalSpectral Bias Higher-Order GNNs
Department of Computer Science, University of California Santa Barbara
Graph Vector Field: A Unified Framework for Multimodal Health Risk Assessment from Heterogeneous Wearable and Environmental Data Streams
Mar 30, 2026Silvano Coletti, Francesca FallucchiHigher-Order GNNs Dynamic Graph Learning
Universit`a degli Studi Guglielmo Marconi, Roma, Italy · Chelonia SA, Lugano, Switzerland
TopoOR: A Unified Topological Scene Representation for the Operating Room
Mar 10, 2026Tony Danjun Wang, Ka Young Kim, Tolga Birdal +2Higher-Order GNNs Topological Representation Learning
Technical University of Munich, Munich, Germany · Munich Center for Machine Learning, Germany · Kyung Hee University, South Korea +1
Invariant-Stratified Propagation for Expressive Graph Neural Networks
Mar 2, 2026Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe +1Over-Smoothing in GNNs Higher-Order GNNs
Department of Mechanical Engineering, University of Melbourne, Melbourne, Australia · School of Computing, Australian National University, Canberra, Australia · Data61, CSIRO, Canberra, Australia
FloydNet: A Learning Paradigm for Global Relational Reasoning
Jan 27, 2026Jingcheng Yu, Mingliang Zeng, Qiwei YeGraph Neural Networks Relational Reasoning
Beijing Academy of Artificial Intelligence

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PANDA: Prior-guided Attentional Dual-path Architecture

Rishabh Jain, Federico Belotti, Stefano Coniglio, Pietro Lio+1

University of Cambridge, UK · University of Bergamo, Italy · Independent Researcher

Hypergraph Neural Networks Higher-Order GNNs Message Passing Neural Networks Attention Mechanisms Hybrid Attention

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Hypergraph Neural Networks (HNNs), designed to model higher-order relations beyond pairwise interactions, typically rely either on fixed structural weights or on normalised attention. Fixed weights can propagate misleading information in heterophilic settings, while normalisation makes attention coefficients competitive, in the sense that, in a message-passing framework, increasing the weight of one sender must reduce the weight of others in the same aggregation set. Our main contribution is the introduction of PANDA (Prior-guided AttentioNal Dual-path Architecture), a plug-in for two-stage message passing HNNs applicable to both undirected and directed settings. The main path interpolates between learned receiver-normalised attention and structural incidence priors, while a non-competitive (not normalised) auxiliary path lets each sender contribute its own transformed information. We also show that, with fixed structural coefficients, PANDA recovers a certain complex-valued convolution operator adopted in previous works. Across four HNNs spanning spatial and spectral methods in both directed and undirected settings, plugging PANDA in yields an average improvement of 5.18 percentage points across eight real-world benchmark datasets. The code is available at https://github.com/jainris/panda.