Heterogeneous GNNs

GNN: Graph Neural Network

Heterogeneous GNNs study how to represent and make predictions from graphs whose nodes and edges have different types, such as patients and diagnoses or users and transactions. Common approaches use relation-aware message passing, attention over typed neighbors, meta-paths, temporal encodings, and contrastive or self-supervised pretraining. These models are applied to tasks including recommendation, fraud detection, biomedical prediction, and power-system analysis, with evaluation spanning both established graph benchmarks and domain-specific data. A central challenge is balancing scalable learning and transfer across graphs with preserving type-specific meaning, especially when relations are noisy, sparse, or heterophilous.

Latest papers

78 papers9 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

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
Graph Topology Information Enhanced Heterogeneous Graph Representation Learning
Apr 7, 2026He Zhao, Zhiwei Zeng, Yongwei Wang +1Graph Representation Learning Graph Structure Learning
Nanyang Technological University, Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Singapore · Nanyang Technological University, Singapore
ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment
Mar 30, 2026Tran Duong Minh Dai, Triet Huynh Minh Le, M. Ali Babar +2Software Vulnerability Detection Multimodal Graph Learning
Information Security Lab, University of Information Technology, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam · School of Computer Science and Information Technology, Adelaide University, Adelaide, Australia
Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference
Mar 24, 2026Zhiceng Shi, Changmiao Wang, Jun Wan +1Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images
Yunnan University, Kunming 650500, China · Shenzhen Research Institute of Big Data, Shenzhen 518172, China · Zhongnan University of Economics and Law, Wuhan 430073, China
UniHetCO: A Unified Heterogeneous Representation for Multi-Problem Learning in Unsupervised Neural Combinatorial Optimization
Mar 12, 2026Kien X. Nguyen, Ilya SafroCombinatorial Optimization Neural Combinatorial Optimization
Department of Computer and Information Sciences, University of Delaware, Newark DE 19716, USA
VeriHGN: Heterogeneous Graph-Based Congestion Prediction for Chip Layout Verification
Mar 10, 2026Runbang Hu, Bo Fang, Bingzhe Li +1Electronic Design Automation Heterogeneous GNNs
The University of Texas at Arlington, Arlington, Texas, USA · The University of Texas at Dallas, Dallas, Texas, USA
Linguistically Informed Graph Model and Semantic Contrastive Learning for Korean Short Text Classification
Mar 4, 2026JaeGeon Yoo, Byoungwook Kim, Yeongwook Yang +1Text Classification Heterogeneous GNNs
Dept. of Data Science, Kangwon National University, Chuncheon 24341, Republic of Korea · Dept. of Computer Science and Engineering, Gangneung-Wonju National University, Wonju 26403, Republic of Korea
Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
Mar 3, 2026Jiaqi Wang, Zhiguang Cao, Peng Zhao +4Flexible Job Shop Scheduling Job Shop Scheduling
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University · College of Software, Jilin University · Singapore Management University +2
Tensegrity Robot Endcap-Ground Contact Estimation with Symmetry-aware Heterogeneous Graph Neural Network
Mar 3, 2026Wenzhe Tong, Yicheng Jiang, Chi Zhang +2Tensegrity Robotics Robotics
Robotics Department, University of Michigan, Ann Arbor, MI 48109 USA
MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training
Feb 26, 2026Lianze Shan, Jitao Zhao, Dongxiao He +3Graph Representation Learning Heterogeneous GNNs
College of Intelligence and Computing, Tianjin University, Tianjin, China · Departments of Health Technology & Informatics; Data Science & Artificial Intelligence; and Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks
Feb 24, 2026Xuanhao Mu, Jakob Geiges, Nan Liu +2Geospatial Representation Learning Heterogeneous GNNs
Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering
Feb 23, 2026Wuzhenghong Wen, Bowen Zhou, Jinwen Huang +5Knowledge Graph Question Answering Temporal QA
School of Internet of Things, Nanjing University of Posts and Telecommunications · Faculty of Medicine & Health, University of New South Wales, Sydney, Australia · Beijing Information Science and Technology University +2
GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations
Feb 21, 2026Osman Onur Kuzucu, Tunca DoğanHeterogeneous GNNs Biomedical KGs
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University, 06800, Ankara, Turkey. · Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, 06800, Ankara, Turkey. · Dept. of Health Informatics, Institute of Informatics, Hacettepe University, 06800, Ankara, Turkey.
DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs
Dec 13, 2025Jiawen Chen, Yanyan He, Qi Shao +4Heterogeneous GNNs Knowledge Graphs
Jiangsu Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing 210096, China · Jiangsu Key Laboratory of Networked Collective Intelligence, School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China · State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences, Beijing 100190, China +1
CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?
Dec 12, 2025Jie Wang, Zheng Yan, Jiahe Lan +2Temporal GNNs Heterogeneous GNNs
State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University · Hangzhou Institute of Technology, Xidian University · Department of Computer Science, Purdue University
Attention Enhanced Entity Recommendation for Intelligent Monitoring in Cloud Systems
Oct 23, 2025Fiza Husain, Anson Bastos, Anjaly Parayil +4Graph Attention Networks Heterogeneous GNNs
Microsoft
HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning
May 23, 2025Guiquan Sun, Xikun Zhang, Jingchao Ni +1Heterogeneous GNNs Replay-Based Continual Learning
School of Computing, University of Connecticut · RMIT University, Melbourne · Department of Computer Science, University of Houston
Heterogeneous Sheaf Neural Networks
Sep 12, 2024Luke Braithwaite, Alessio Borgi, Gabriele Onorato +4Heterogeneous GNNs Sheaf Neural Networks
Department of Computer Science and Technology, University of Cambridge · Department of Computer, Control and Management Engineering, Sapienza University of Rome · Department of Electrical and Computer Engineering, Northeastern University

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HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment

Changdao Chen, Qinqiuhong Ye, Hao Chen, Jinyu Wang

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

Heterogeneous GNNs Hypergraph Neural Networks Driver Monitoring Temporal Video Understanding

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It remains challenging to assess driver fatigue from untrimmed videos under constrained computational budgets, due to the difficulty of modeling long-range temporal dependencies in subtle facial expressions. Some existing approaches rely on computationally heavy architectures, whereas others employ traditional lightweight pairwise graph networks, despite their limited capacity to model high-order synergies and global temporal context. Therefore, we propose HST-HGN, a novel Heterogeneous Spatial-Temporal Hypergraph Network driven by Bidirectional State Space Models. Spatially, we introduce a hierarchical hypergraph network to fuse pose-disentangled geometric topologies with multi-modal texture patches dynamically. This formulation encapsulates high-order synergistic facial deformations, effectively overcoming the limitations of conventional methods. In temporal terms, a Bi-Mamba module with linear complexity is applied to perform bidirectional sequence modeling. This explicit temporal-evolution filtering enables the network to distinguish highly ambiguous transient actions, such as yawning versus speaking, while encompassing their complete physiological lifecycles. Extensive evaluations across diverse fatigue benchmarks demonstrate that HST-HGN achieves state-of-the-art performance. In particular, our method strikes a balance between discriminative power and computational efficiency, making it well-suited for real-time in-cabin edge deployment.