Node Classification

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Overview

Node classification predicts categories for entities such as users, papers, accounts, or neurons, using their attributes and relationships. Common approaches include graph neural networks with message passing, label propagation, graph transformers, and methods that separate homophilic from heterophilic connections; text-attributed graphs also use language models and graph foundation models. Work covers transductive and inductive prediction, as well as few-shot learning, class imbalance, noisy labels, distribution shift, and calibrated uncertainty. A central challenge is determining when added structure or pretrained models truly help: results can depend on graph type, data splits, and careful tuning, and strong conventional GNN baselines remain difficult to beat consistently.

Latest papers

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99 papers13 papers in the last four weeks, down 13% on the four weeks before. 0.1% of all new papers.

  1. Graph Representation Learning via Adaptive Expectile Aggregation

    Oct 27, 2026Tianyi Zhu, Wei LanGraph Representation LearningNode Classification

    Department of Statistics, Colorado State University Fort Collins, CO 80523, USA · Laboratory for Data Intelligence and Innovation in Finance and Economics and Center of Statistical Research Southwestern University of Finance and Economics, Chengdu, China

  2. DualGSL: Prior-Guided Graph Structure Learning with Node-Adaptive Prediction Fusion

    Oct 23, 2026Yuxia Sun, Yutszyuk Wong, Huihong Chen +3Graph Structure LearningNode Classification

    the College of Information Science and Technology, Jinan University, Guangzhou 510632, China · Jinan University, Guangzhou 510632, China · Sun Yat-sen University +1

  3. Big Claims, Small Margins: Do Reported GraphLLM Gains Survive Simple Baselines?

    Oct 20, 2026Michal PodstawskiLLMs for Graph LearningText-Attributed Graph Learning

    NASK National Research Institute, Warsaw, Poland.

  4. A Label Noise Robust Algorithm for Graph Data using Neighborhood Label Distribution

    Oct 16, 2026Pintu Kumar, Nandyala HemachandraGraph Neural NetworksNoisy-Label Learning

    Indian Institute of Technology Bombay

  5. Money-Mule Detection as a Relational Learning Problem: A Study on the RBIH NFPC Dataset

    Oct 15, 2026Owais Ahmad, Anirudh DeodharFinancial Fraud DetectionAnti-Money Laundering Detection

    Applied Research, Quantiphi, Marlborough, MA 01752, USA

  6. When Does the Dependency Graph Help Code-to-Architecture Mapping?

    Oct 14, 2026Rakhshanda Jabeen, Morgan Ericsson, Jonas Nordqvist +1Software EngineeringGraph-Based Semi-Supervised Learning

    Department of Computer Science, Linnaeus University, Växjö, Sweden · Electrolux Professional AB, Ljungby, Sweden · Department of Mathematics, Linnaeus University, Växjö, Sweden

  7. PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification

    Oct 10, 2026Amin Najafgholizadeh, Mohsen AfsharchiBenchmark ContaminationSynthetic Data Evaluation

    University of Zanjan

  8. Constructing Structured Decision Sources for Consensus-Based Pseudo-Label Learning

    Oct 8, 2026Long WangPseudo-LabelingEnsemble Diversity

  9. Node-level Graph Neural Architecture Search Framework

    Oct 7, 2026Lintao Yanga, Sirui Lia, Yaqing Wang +4Graph Neural NetworksNeural Architecture Search

    College of Information Science and Engineering, Ningbo University, Ningbo, 315211, China · Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK · School of Artificial Intelligence, Jilin University, Changchun, 130015, China

  10. Valid for Free: Homophily-Gated Conformal Prediction for Training-Free Node Classification with Tabular Foundation Models

    Oct 6, 2026Nguyen Duy Long, Phung Minh Hien, Nguyen Trong Viet +1Tabular Foundation ModelsConformal Prediction

    Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam · Faculty of Engineering, School of Computer Science, The University of Sydney, Sydney, Australia · Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

  11. Rethinking Epistemic Uncertainty in Node Classification through Information Growth

    Oct 2, 2026Emma Meneghini, Francesco Ferrini, Bruno Lepri +2Epistemic UncertaintyEvidential Deep Learning

    University of Trento · Fondazione Bruno Kessler, FBK · University of Tromsø

  12. NodeGround: A Node Classification Benchmark in the Graph Foundation Model Era

    Sep 30, 2026Jinmo Lee, Dooho Lee, Minho Jeong +1Graph Foundation ModelsNode Classification

    Nums AI

  13. Message Passing Does More with Less for In-Context Learning on Graphs

    Sep 29, 2026Dooho Lee, Jinmo Lee, Minho Jeong +2In-Context LearningGraph Neural Networks

    Nums AI · KAIST · Seoul National University

  14. Propagate, Then Sharpen: Post-Hoc Refinement of Frozen Node Classifiers

    Sep 28, 2026Preben Johnsen Bentdal, Nello Blaser, Xue-Cheng TaiGraph-Based Semi-Supervised LearningClassification

    Department of Informatics, University of Bergen · Norwegian Research Centre (NORCE)

  15. Extremely Fast and Compact Binary Graph Representations via Randomized Operator Sketching

    Sep 26, 2026Srajan Agarwal, Megha P, Bikas C Das +2Graph Representation LearningRandomized Sketching

    School of Data Science, IISER Thiruvananthapuram, India · Department of Physical Sciences, IISER Berhampur, India

  16. Learning the Graph and the Embedding Together: Classifier-Independent Rewiring for Heterophilic Node Classification

    Sep 26, 2026Harshit Kumar, Sujan Chakraborty, Priyanka Saha +2Graph RewiringGraph Structure Learning

    School of Data Science, Indian Institute of Science Education and Research Thiruvananthapuram, Thiruvananthapuram 695551, India

  17. HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration

    Sep 26, 2026Inwoo Tae, Yoontae Hwang, Yongjae LeeModel CalibrationPost-Hoc Calibration

    UNIST · Pusan National University · UNIST LinqAlpha

  18. Topology-Adaptive Hyperbolic Graph Attention Networks Guided by the Hyperbolic Sombor Index

    Sep 26, 2026Haifang Cao, Boan Tao, Xiyuan Gao +3Graph Neural NetworksGraph Attention Networks

    Tianjin University Tainjin, China

  19. ICE: Task-Aligned Clifford Latent Fields for Multimodal Graph Foundation Models

    Sep 24, 2026Xunkai Li, Xu Wang, Yinlin Zhu +4Graph Foundation ModelsMultimodal Graph Learning

    Department of Computer Science, Beijing Institute of Technology, Beijing, China · School of Airspace Science and Engineering, Shandong University, WeiHai, China · School of Computer Science and Engineering, Sun Yat-sen University, GuangZhou, China

  20. MOVE: Multimodal Open-world Verification and Expansion for Graph Learning

    Sep 24, 2026Zekai Chen, Jiayang Xing, Xun Wu +7Multimodal Graph LearningNode Classification

    Beijing Institute of Technology · Shandong University

  21. Local Evidence and Geometric Readout Repair in Trained GNNs

    Sep 22, 2026Nadi Tomeh, Hugo AttaliGraph Neural NetworksNode Classification

    Université Sorbonne Paris Nord, CNRS, Laboratoire d’Informatique de Paris Nord, LIPN, F-93430 Villetaneuse, France

  22. PreGS: A Parameter-Transfer-Based Multi-Expert Graph Neural Network for Node Classification

    Sep 22, 2026Zhicong Cai, Yinglong Zhang, Xiaoying Hong +2Graph Neural NetworksMixture-of-Experts Models

    College of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China

  23. Graph Domain Adaptation Does Not End with Representation Learning

    Sep 22, 2026Ziqian Liu, Yongxue Xu, Enze Zhang +3Unsupervised Domain AdaptationGraph Representation Learning

    Sun Yat-sen University, China · University of Wisconsin–Madison, USA · Jiangsu University, China +2

  24. Provable Guarantees for Spectral Structured Prediction

    Sep 16, 2026Violet Zheng, Jean HonorioSpectral MethodsNode Classification

    CIS, The University of Melbourne

  25. Not All Nodes Are Created Equal: Homophily-Aware Stratification for Stable GNN Evaluation

    Sep 16, 2026Naga Venkata Sai Jitin Jami, Thomas Altstidl, Sebastian Hoefler +4Graph Neural NetworksNode Classification

    Chair for Machine Learning in Medicine, Universität Bayreuth · Department Artificial Intelligence in Biomedical Engineering, FAU Erlangen-Nürnberg · Chair of AI-supported Therapy Decisions, Ludwig-Maximilians-Universität München +1

  26. Online Bayesian Node Classification on Inductive Graphs under Distribution Shift

    Sep 12, 2026Jinwen Xu, Gonzalo Mateos Buckstein, Qin LuNode ClassificationGraph Neural Networks

    School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA · Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA

  27. HOPE: Heterophily-Aware Open-Set Node Classification with Pseudo-Extrapolation

    Sep 8, 2026Yumeng Dai, Yue Tan, Yixin Liu +3Graph Neural NetworksNode Classification

    Xi’an Jiaotong University, Xi’an, China · Griffith University, Brisbane, Australia

  28. Towards Unified Multimodal Graph Foundation Model: A Bridge-Router-Adapter Based Approach

    Sep 6, 2026Sirui Zhang, Yubing Zhou, Xunkai Li +6Graph Foundation ModelsMultimodal Graph Learning

    Beijing Institute of Technology Beijing, China

  29. Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks

    Sep 6, 2026Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe +1Over-Smoothing in GNNsGraph Neural Networks

    Department of Mechanical Engineering University of Melbourne Melbourne, Australia · School of Computing Australian National University Canberra, Australia · Data61, CSIRO Canberra, Australia

  30. Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution

    Aug 27, 2026Yingqi Feng, Yufei Tang, Min Shi +1GNN ExplainabilityMulti-Label Classification

    Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA · School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA, USA