Heterogeneous GNNs

GNN: Graph Neural Network

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
May 22, 2026Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim +1Neural Surrogate Modeling Heterogeneous GNNs
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA
Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalities
May 16, 2026Sha Tao, Jiao Pan, Yu Guo +1Missing-Modality Learning Heterogeneous GNNs
University of Science and Technology Beijing, Beijing, China
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
May 15, 2026Peiyuan Li, Yongqi Huang, Jitao Zhao +3Heterogeneous GNNs Few-Shot Learning
School of Computer Science and Technology, Tianjin University, Tianjin, China · Department of Health Technology and Informatics, and Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Graph-Driven Cross-Industry Real-Time Monitoring Framework for Anti-Money Laundering Detection in Converged Mobility-Energy Supply Chain Networks
May 13, 2026Rong Liu, Xiaojun Xiao, Zhanqing SuTemporal GNNs Heterogeneous GNNs
Sol Price School of Public Policy, University of Southern California, Los Angeles, USA · Boston University, Boston, USA · Johns Hopkins University, Washington, USA
Hierarchical Attention-based Graph Neural Network with Relevance-driven Pruning
May 10, 2026Seungwoo KumGraph Attention Networks Heterogeneous GNNs
Korea Electronics Technology Institute (KETI) Seongnam, Republic of Korea
HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily
May 6, 2026Xinyi Li, Ming Li, Lu Bai +5Heterogeneous GNNs Node Classification
Zhejiang Normal University · Beijing Normal University · Central University of Finance and Economics +2
Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment
May 1, 2026Ziyu Zheng, Yaming Yang, Zhe Wang +2Heterogeneous GNNs Cross-Domain Transfer Learning
School of Computer Science and Technology, Xidian University, Xi’an, China 710071
ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks
May 1, 2026Ta Thanh Thuy, Jiaqi Zhu, Xuan Liu +6Social Media Analysis Heterogeneous GNNs
1Nanyang Technological University · 2NVIDIA · 3Nanjing University +3
Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach
Apr 30, 2026Yihan Zhang, Ercan E. KuruogluGraph Structure Learning Graph Representation Learning
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
TypeBandit: Type-Level Context Allocation and Reweighting for Effective Attribute Completion in Heterogeneous Graph Neural Networks
Apr 30, 2026Ta-Yang Wang, Rajgopal Kannan, Viktor PrasannaGraph Representation Learning Heterogeneous GNNs
University of Southern California, Los Angeles, USA · DEVCOM Army Research Office, Los Angeles, USA
TRAVELFRAUDBENCH: A Configurable Evaluation Framework for GNN Fraud Ring Detection in Travel Networks
Apr 22, 2026Bhavana SajjaHeterogeneous GNNs Financial Fraud Detection
FOCAL-Attention for Heterogeneous Multi-Label Prediction
Apr 21, 2026Chenghao Zhang, Qingqing Long, Ludi Wang +3Graph Attention Networks Multi-Label Classification
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China
LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
Apr 18, 2026Yikun Wang, Yujie Wan, Wei Zuo +43D Gaussian Splatting Heterogeneous GNNs
The University of Hong Kong, Hong Kong, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · Southern University of Science and Technology, Shenzhen, China +2
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
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

May 22, 2026, cs.LGJ/K move · Enter open · S save

Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids

Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim, Teja Kuruganti

Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA

Neural Surrogate Modeling Heterogeneous GNNs Optimal Power Flow Graph Foundation Models Constrained Optimization Power Systems

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Fast and reliable optimal power flow (OPF) approximation is important for power system operation, yet heterogeneous OPF graph models are often evaluated either with architecture-specific implementations or on limited training corpora. This paper presents a large-scale heterogeneous graph-learning workflow, built on HydraGNN, for data-driven OPF surrogate modeling and graph foundation-model (GFM) development. The workflow preserves buses, generators, loads, shunts, alternating-current (AC) lines, transformers, and device-to-bus relations and provides a common implementation for distributed preprocessing, multi-graphics-processing-unit (GPU) training, hyperparameter optimization (HPO), and downstream adaptation. Using approximately three million heterogeneous graphs from ten Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms (PGLib-OPF) cases spanning 14 to 13,659 buses, we compare six heterogeneous graph neural network (GNN) families under a common HPO campaign on Frontier, resulting in two compact HeteroSAGE and HeteroHEAT models (~1.6-1.7 million parameters) that achieve the lowest observed validation MSE. Strong and weak scaling using up to 1,024 Frontier nodes show that 256 nodes (corresponding to 1,024 AMD MI250X GPUs) provides the best time-to-solution balance for the measured workload. Two downstream tasks evaluate task adaptation on the IEEE 118-bus case: a classification task to distinguish nominal operating conditions from artificially generated extreme-overload conditions and N-1 single-component-outage regression to predict bus voltage magnitude and phase angle. Partial fine-tuning provides the strongest balance between predictive performance and computational requirements.

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