Hypergraph Neural Networks

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6 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
May 17, 2026Keshu Wu, Sixu Li, Zihao Li +3Graph Structure Learning Incomplete Data Imputation
Texas A&M University · University of Wisconsin-Madison
Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions
May 15, 2026Kyrie Zhao, Zehong Wang, Tianyi Ma +5Feature Interaction Modeling Hypergraph Neural Networks
University of Notre Dame · Stanford University · Yale University +2
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
Anchor-guided Hypergraph Condensation with Dual-level Discrimination
May 11, 2026Fan Li, Xiaoyang Wang, Chen Chen +1Hypergraph Neural Networks Dataset Condensation
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia · School of Artificial Intelligence, Shenzhen University, Shenzhen, China.
Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs
May 6, 2026Zhenchao Sun, Shuai Ma, Ping Lu +1Hypergraph Neural Networks Constraint Satisfaction
SKLCCSE Lab, Beihang University, Beijing, China.
UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation
Apr 30, 2026Shuokun Cheng, Jinghao Shi, Kun SunContrastive Learning Image Segmentation
School of Computer Sciences, China University of Geosciences (Wuhan), Wuhan 430074, Hubei, China
HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment
Apr 9, 2026Changdao Chen, Qinqiuhong Ye, Hao Chen +1Heterogeneous GNNs Hypergraph Neural Networks
School of Computer Science and Technology, Xi’an Jiaotong University Xi’an, Shaanxi, China · School Of Mechanical Engineering, Xi’an Jiaotong University Xi’an, Shaanxi, China · Xi’an Jiaotong University Health Science Center Xi’an, Shaanxi, China
SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease
Mar 20, 2026Ruiying Chen, Yutong Wang, Houliang Zhou +3Irregular Time-Series Modeling Alzheimer's Disease
Lehigh University, Bethlehem, PA, USA · New York University, New York, NY, USA · University of Pennsylvania, Philadelphia, PA, USA
M2I2HA: Multi-modal Object Detection Based on Intra- and Inter-Modal Hypergraph Attention
Jan 21, 2026Xiaofan Yang, Yubin Liu, Wei Pan +5Cross-Modal Alignment Hypergraph Neural Networks
Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud
Jan 16, 2026Rongkun Cui, Nana Zhang, Kun Zhu +1Long-Tail Learning Multi-View Learning
School of Computer Science and Technology, Tongji University Shanghai, China · School of Computer Science and Technology, Donghua University Shanghai, China
Learning Constraints-Based Adaptive Hypergraph Neural Networks for Solving Vehicle Routing Problems
Mar 13, 2025Zhenwei Wang, Tiehua Zhang, Jing Liu +3Constrained RL Vehicle Routing
University of Nottingham Ningbo China, 199 East Taikang Road, Ningbo, 315100, Zhejiang, China · Tongji University, 1239,Siping Road, Shanghai, 200092, Shanghai, China · State Key Laboratory for Novel Software Technology, Nanjing University, 163,Xianlin Road, Nanjing, 210023, Jiangsu, China +1
Hypergraph-Enhanced Dual Convolutional Network for Bundle Recommendation
Dec 18, 2023Yang Li, Kangbo Liu, Yaoxin Wu +2Recommender Systems Hypergraph Neural Networks
Northwestern Polytechnical University, China · Nanyang Technological University, Singapore

May 17, 2026, cs.LGJ/K move · Enter open · S save

Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement

Keshu Wu, Sixu Li, Zihao Li, Zhiwen Fan+2

Texas A&M University · University of Wisconsin-Madison

Graph Structure Learning Incomplete Data Imputation Learning with Missing Data Hypergraph Neural Networks Hypergraph Learning

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Sensor networks increasingly govern modern infrastructure, yet the data they lose are rarely missing in the uniform-random patterns assumed by standard imputation benchmarks. Loop detectors go offline during calibration, roadside cabinets silence clusters of nearby sensors, and newly installed instruments provide no history. Such failures create structured absences whose values are constrained by higher-order relations among groups of sensors, not merely by pairwise proximity. Existing low-rank and graph-based methods often miss this collective structure and can fail when missingness becomes coherent. We introduce Multi-Scale Hypergraph Laplacians (MSHL), a two-stage framework for learning higher-order structure from incomplete spatiotemporal observations. The Discovery stage builds a multi-scale hypergraph from complementary topology and residual-correlation evidence, with an observation-only selector that adapts to the supported interaction scale. The Refinement stage adds a small hypergraph-conditioned residual network that is safe by construction: it learns nonlinear corrections where informative residual features exist and defers to the linear estimate where they do not. We prove that MSHL represents group-conservation patterns inaccessible to pairwise graph priors, adapts to the best fixed scale up to a logarithmic factor, transfers this advantage to held-out imputation error, and admits a one-sided refinement guarantee. On two real traffic networks evaluated across scattered cell missingness, contiguous block outages, and whole-sensor blackouts at five rates, MSHL improves over a pairwise-graph baseline whenever higher-order structure is identifiable and otherwise matches it within sampling noise. The results point to a broader principle for reliable infrastructure learning: missing data should be treated not as isolated entries to fill, but as evidence of structure to discover.

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