Graph Neural Networks

Also known as GNN

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88 papers in the last four weeks, up 31% on the four weeks before. 0.5% of all new papers.

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Multi-Label Node Classification with Label Influence Propagation
Jul 1, 2026Yifei Sun, Zemin Liu, Bryan Hooi +4Multi-Label Classification Graph Neural Networks
Zhejiang University · National University of Singapore · Capital One +1
T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation
Jun 29, 2026Huy Truong, Alexander Lazovik, Victoria DegelerGraph Neural Networks Test-Time Adaptation
Bernoulli Institute, University of Groningen, Groningen, The Netherlands · Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
Rethinking Generative Reconstruction Attacks against Graph Neural Network Models
Jun 29, 2026Adebayo Keji, Sayanton DibboGraph Neural Networks
Department of Computer Science, The University of Alabama
Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop
Jun 29, 2026Chenmu Zhang, Boris I. YakobsonGraph Neural Networks Large Language Model-Guided Optimization
Department of Materials Science and NanoEngineering Rice University Houston, TX 77005, USA
Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection
Jun 26, 2026Liming Liu, Chao Hu, Mingfei Lu +3Graph Neural Networks Graph SSL
Central South University, Changsha, China · University of Technology Sydney, Australian Artificial Intelligence Institute, Sydney, Australia
Graph Neural Networks Applications Across Domains: All Insights You Need
Jun 25, 2026Abderaouf BahiGraph Neural Networks Over-Squashing
Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University, P.O. Box 73, El Tarf 36000, Algeria
A General Framework for Learning Algebraic Properties from Cayley Graphs using Graph Neural Networks
Jun 24, 2026Tal WeissblatGraph Neural Networks
Tal Weissblat
Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need
Jun 24, 2026Nathan Painchaud, Tristan Habémont, Morgane des Ligneris +6Graph Neural Networks Clinical Risk Prediction
INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69621, Lyon, France · Université Jean Monnet Saint-Étienne, CHU Saint-Étienne, Mines Saint-Étienne, INSERM, SAINBIOSE U1059, CIC 1408, Département de Médecine Vasculaire et Thérapeutique, F-CRIN INNOVTE network, all in F-42055, Saint-Étienne, France · Department of Pneumology, CHU Saint-Etienne, UJM Saint-Etienne, France +3
Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks
Jun 24, 2026Rupesh Raj Karn, Johann Knechtel, Ozgur SinanogluGraph Neural Networks Cybersecurity
Center for Cyber Security, New York University, Abu Dhabi, UAE.
SAGE-Nav: Leveraging LLM Planning and Alignment Fusion for Hierarchical Scene Graph-Guided Navigation
Jun 24, 2026Hao Su, Yuehao Huang, Yukai Ma +2Robot Policy Learning Graph Neural Networks
Zhejiang University, Hangzhou, China
A Fair Evaluation of Graph Foundation Models for Node Property Prediction
Jun 23, 2026Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev +1Graph Neural Networks Graph Foundation Models
1HSE University · 2Yandex Research
Convex--Concave Quadratic Spectral Filtering for Graph Neural Networks
Jun 23, 2026Ranhui Yan, Jia Cai, Mengzhu Chen +1Graph Neural Networks Spectral Methods
School of Computing and Artificial Intelligence, Guangzhou Xinhua University, Guangzhou, Guangdong, China · School of Statistics and Data Science, Guangdong University of Finance and Economics,2026 Guangzhou, Guangdong, China
A Spectral Theory of Normalized Corrected GNN Propagation
Jun 22, 2026Qihan Chen, Wei Li, Meng Qin +1Graph Neural Networks Spectral Methods
Center for Discrete Mathematics, Fuzhou University · School of Computer and Information Science, Fujian Agriculture and Forestry University · Department of Strategic & Advanced Interdisciplinary Research, Pengcheng Laboratory (PCL), Shenzhen, Guangdong, China
SuperCond-GNN: Scalable Graph Neural Network Surrogate for Superconducting Circuit Simulations
Jun 22, 2026Nandana Menon, Giorgio ValloneNeural Surrogate Modeling Graph Neural Networks
Acclerator Technology & Applied Physics Division Lawrence Berkeley National Laboratory Berkeley, CA · Engineering Division Lawrence Berkeley National Laboratory Berkeley, CA
Rethinking Molecular Graph Backdoors under Chemistry-aware Admission
Jun 22, 2026Thinh T. H. Nguyen, Sze Jue Yang, Khoa D. Doan +2Graph Neural Networks Backdoor Attacks
VinUniversity, Hanoi, Vietnam · Universiti Malaya
Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
Jun 22, 2026Xinxi Chen, Junyang Chen, Yiqun Zhang +2Graph Structure Learning Graph Neural Networks
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Learning Graphs through Continuous Information Entropy Fields
Jun 22, 2026Hui Cong, Bo Sun, Ziheng Jiao +1Graph Structure Learning Graph Neural Networks
School of Information Engineering, Chang’an University, Shaanxi, Xi’an, China · AUTHORERR: Missing \icmlaffiliation.
GeoRouteNet: Geometry-Enhanced Non-Autoregressive Neural Solver for the Traveling Salesman Problem
Jun 22, 2026Xiang LiTraveling Salesperson Problem Graph Neural Networks
College of Computer Science, Yangtze University, Jingzhou, China
Adaptive Recurrent Message Passing for Test Time Computing on Graphs
Jun 21, 2026Junshu Sun, Wanxing Chang, Qingming Huang +1Adaptive Inference Graph Neural Networks
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · DAMO Academy, Alibaba Group, Hangzhou, China.
Early-Exit Graph Neural Networks for Link Prediction
Jun 20, 2026Roman Knyazhitskiy, Andrea Giuseppe Di FrancescoEfficient Neural Network Inference Graph Neural Networks
University of Cambridge · Sapienza University of Rome
A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation
Jun 18, 2026Antonio Marino, Claudio Pacchierotti, Paolo Robuffo GiordanoMulti-Agent Coordination Graph Neural Networks
Universitè de Rennes, CNRS, Inria, IRISA, France · CNRS, Rennes, France
Hierarchical Pooling for Sheaf Neural Networks
Jun 18, 2026Dionisia Naddeo, Carlo Abate, Pietro Liò +2Graph Neural Networks Graph Coarsening
UiT The Arctic University of Norway · UiT The Arctic University of Norway NORCE Norwegian Research Centre AS
Physics-Guided Dual-Stream Heterogeneous Graph Neural Network for Predicting Full-Field Structural Response of Stiffened Panels
Jun 18, 2026Yuecheng Cai, Jasmin JelovicaNeural Surrogate Modeling Graph Neural Networks
Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada · Department of Civil Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Towards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation
Jun 18, 2026Yanning Wang, Zhiyong Zhou, Zhouyu Liu +2Graph Neural Networks Link Prediction
The Hong Kong University of Science and Technology (Guangzhou), China · Zhejiang University, China · Mainz University of Applied Sciences, Germany
Enhancing Graph Neural Networks Using Proximity Graphs for Dust Source Emission Forecasting
Jun 18, 2026Maryam Sanisales, Zahed Rahmati, Ali Darvishi Boloorani +1Graph Neural Networks Spatiotemporal Forecasting
Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran · Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks
Jun 17, 2026Nanhong Liu, Mucun Sun, Jie ZhangGraph Neural Networks Electric Vehicle Charging
Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA · Idaho National Laboratory, Idaho Falls, ID 83415, USA · Department of Mechanical Engineering and (Affilated) Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Acceleration of an algebraic multigrid pressure solver using graph neural networks
Jun 17, 2026Eric Chillón, Artur K. Lidtke, Nguyen Anh Khoa Doan +1Graph Neural Networks PDE Solving
Faculty of Mechanical Engineering, Delft University of Technology, The Netherlands · Maritime Research Institute Netherlands, The Netherlands · Department of Aeronautics, Imperial College London, United Kingdom
AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network
Jun 17, 2026Bolin Shen, Ziwei Huang, Zhiguang Cao +1Traveling Salesperson Problem Graph Neural Networks
Florida State University · Tallahassee, United States · Singapore Management University +1
Enhanced Graph Neural Networks using K-Hop Gaussian Diffusion
Jun 16, 2026Xuling Zhang, Peng Wang, Daiyan Li +3Graph Neural Networks Graph Diffusion
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · Southern University of Science and Technology, Shenzhen, China
Geometrical fairness in graph neural networks
Jun 16, 2026Arturo Pérez-Peralta, Sandra Benítez-Peña, Blas Kolic +1Graph Neural Networks Graph Representation Learning
Department of Statistics, University Carlos III of Madrid, Spain · uc3m-Santander Big Data Institute

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Multi-Label Node Classification with Label Influence Propagation

Yifei Sun, Zemin Liu, Bryan Hooi, Yang Yang+3

Zhejiang University · National University of Singapore · Capital One+1

Multi-Label Classification Graph Neural Networks Node Classification

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Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI networks with multiple functions and users in social or e-commerce networks exhibiting diverse interests. Tackling multi-label node classification (MLNC) on graphs has led to the development of various approaches. Some methods leverage graph neural networks (GNNs) to exploit label co-occurrence correlations, while others incorporate label embeddings to capture label proximity. However, these approaches fail to account for the intricate influences between labels in non-Euclidean graph data. To address this issue, we decompose the message passing process in GNNs into two operations: propagation and transformation. We then conduct a comprehensive analysis and quantification of the influence correlations between labels in each operation. Building on these insights, we propose a novel model, Label Influence Propagation (LIP). Specifically, we construct a label influence graph based on the integrated label correlations. Then, we propagate high-order influences through this graph, dynamically adjusting the learning process by amplifying labels with positive contributions and mitigating those with negative influence. Finally, our framework is evaluated on comprehensive benchmark datasets, consistently outperforming SOTA methods across various settings, demonstrating its effectiveness on MLNC tasks.