Node Classification

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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.

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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.

ARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification
May 25, 2026VSS Tejaswi Abburi, Saurabh J. Shigwan, Nitin KumarGraph Neural Networks Unsupervised Representation Learning
Department of Computer Science and Engineering, Shiv Nadar Institute of Eminence Deemed to be University, Delhi NCR, India
SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning
May 25, 2026Shubhajit Roy, Anirban DasguptaGraph Representation Learning Temporal Link Prediction
Department of Computer Science and Engineering Indian Institute of Technology Gandhinagar
Fast and Featureless Node Representation Learning with Partial Pairwise Supervision
May 19, 2026Sujan Chakraborty, Saptarshi BejGraph Representation Learning Graph Contrastive Learning
School of Data Science, Indian Institute of Science Education and Research, Thiruvananthapuram, 695551, Kerala, India
Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
May 18, 2026Brown Zaz, Mar Gonzàlez I Català, Ferran Hernandez Caralt +2Graph Neural Networks Graph-Based Semi-Supervised Learning
University of Cambridge
Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification
May 12, 2026Kamel Abdous, Nairouz Mrabah, Mohamed BouguessaGraph Representation Learning Spectral Methods
Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada
A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
May 6, 2026Othmane Kabal, Mounira Harzallah, Fabrice Guillet +2KG Construction Graph Neural Networks
Nantes University, LS2N, Nantes, 44300, France · National Institute of Informatics, Chiyoda-ku, Tokyo, 101-8430, Japan · Institute of Science Tokyo, Tokyo, 152-8550, Japan
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
Rethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?
May 2, 2026Yuxuan Xiao, Shengzhong ZhangMulti-Label Classification Graph Neural Networks
Nanjing University of Aeronautics and Astronautics
Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe
Apr 24, 2026Yuchen Xiong, Swee Keong Yeap, Zhen Hong BanGraph Signal Processing Node Classification
China-ASEAN College of Marine Sciences · Xiamen University Malaysia · Sepang 43900, Selangor, Malaysia +1
F\textsuperscript{2}LP-AP: Fast & Flexible Label Propagation with Adaptive Propagation Kernel
Apr 22, 2026Yutong Shen, Ruizhe Xia, Jingyi Liu +1Graph-Based Semi-Supervised Learning Semi-Supervised Learning
Beijing University of Technology
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
Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
Apr 21, 2026Jeongwhan Choi, Jongwoo Kim, Woosung Kang +1Synthetic Data Pretraining OOD Generalization
KAIST Daejeon, Republic of Korea
Bridge-Centered Metapath Classification Using R-GCN-VGAE for Disaster-Resilient Maintenance Decisions
Apr 20, 2026Takato YasunoNode Classification
Takato Yasuno
HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Apr 19, 2026Ziqing Wang, Kaize DingLLM Alignment Graph SSL
Northwestern University
Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification
Apr 13, 2026Jiajun Zhou, Yadong Li, Xuanze Chen +4Graph Neural Networks Mixture-of-Experts Models
Jiajun Zhou, Yadong Li, Xuanze Chen, Chen Ma, Shanqing Yu and Qi Xuan are with the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China, with the Binjiang Cyberspace Security Institute of ZJUT, Hangzhou, 310056, China, and with the Soovar Technologies Co., Ltd., Hangzhou 310056, China. · Chuang Zhao is with the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China.
Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning
Apr 10, 2026Yi Luo, Xu Sun, Guangchun Luo +1Graph Transformer Graph Heterophily
Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China · School of Computer Science and Engineering, University of Electronic Science and Technology of China · Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province, China
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
Apr 7, 2026Yuanfu Sun, Kang Li, Dongzhe Fan +2LLMs for Graph Learning Text-Attributed Graph Learning
New York University Shanghai · New York University · Tsinghua University
Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
Mar 31, 2026Guan Wang, Shuyin Xia, Lei Qian +4Graph Coarsening Graph Neural Networks
School of Computer Science and Technology, Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Sichuan-Chongqing Co-construction Key Laboratory of Digital Economy Intelligence and Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China. · School of Computer Science and Technology, School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China. · Chongqing Key Laboratory of Brain-Inspired Cognitive Computing and Educational Rehabilitation for Children with Special Needs, Chongqing Normal University, Chongqing, 401331, China. +1
CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization
Mar 25, 2026Bowen Lu, Lianqiang Yang, Teng Li +1OOD Generalization Graph Neural Networks
Anhui University, Hefei, China · Carnegie Mellon University, Pittsburgh, Commonwealth of Pennsylvania, USA
Cost-Sensitive Neighborhood Aggregation for Heterophilous Graphs: When Does Per-Edge Routing Help?
Mar 25, 2026Eyal WeissGraph Neural Networks Graph Heterophily
Computer Science Faculty, Technion — Israel Institute of Technology
Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion
Mar 20, 2026Chengjie Cui, Taihua Xu, Shuyin Xia +3Graph Neural Networks Multi-View Learning
School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, China · Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China · College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China
Position: Spectral GNNs Are Neither Spectral Nor Superior for Node Classification
Mar 19, 2026Qin Jiang, Chengjia Wang, Michael Lones +2Spectral Graph Convolution Node Classification
Department of Computer Science, University of Heriot-Watt, Edinburgh, UK
Gaussian Process Limit Reveals Structural Benefits of Graph Transformers
Mar 18, 2026Nil Ayday, Lingchu Yang, Debarghya GhoshdastidarGNN Oversmoothing Graph Neural Networks
School of Computation, Information and Technology, Technical University of Munich · Graduate School of Information Science and Technology, The University of Tokyo
A Depth-Aware Comparative Study of Euclidean and Hyperbolic Graph Neural Networks on Bitcoin Transaction Systems
Mar 17, 2026Ankit Ghimire, Saydul Akbar Murad, Nick RahimiGraph Neural Networks Hyperbolic Representation Learning
School of Computing Sciences and Computer Engineering University of Southern Mississippi Hattiesburg, MS, 39406, USA
GaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification
Mar 11, 2026Mayur Choudhary, Saptarshi Sengupta, Katerina PotikaLLMs for Graph Learning Text-Attributed Graph Learning
Department of Computer Science, San Jose State University
Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
Mar 10, 2026MoonJeong Park, Seungbeom Lee, Kyungmin Kim +5Transductive Learning Generalization Bounds
Graduate School of Artificial Intelligence, POSTECH, South Korea · CSIRO’s Data61, Canberra, Australia · Department of Computer Science & Engineering, POSTECH, South Korea
$P^2$ GNN: Two Prototype Sets to boost GNN Performance
Mar 10, 2026Arihant Jain, Gundeep Arora, Anoop Saladi +1Graph Neural Networks Message Passing Neural Networks
Amazon, India
Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
Mar 9, 2026Jun Yin, Peng Huo, Bangguo Zhu +4Relational Deep Learning Class-Imbalanced Learning
Department of Data Science and Artificial Intelligence, Hong Kong Polytechnic University, Hong Kong SAR, China · National Super Computing Center, Tianjin, China · School of Computer Science and Engineering, Central South University, Hunan, China +1
Graph Hopfield Networks: Energy-Based Node Classification with Associative Memory
Mar 3, 2026Abinav Rao, Alex Wa, Rishi AthavaleNode Classification Hopfield Networks
VecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention
Feb 23, 2026Jingbo Zhou, Jun Xia, Siyuan Li +11Graph Transformer Node Classification
Zhejiang University Hangzhou, Zhejiang, China · Westlake University Hangzhou, Zhejiang, China · Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China +4

May 25, 2026J/K move · Enter open · S save

ARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification

VSS Tejaswi Abburi, Saurabh J. Shigwan, Nitin Kumar

Department of Computer Science and Engineering, Shiv Nadar Institute of Eminence Deemed to be University, Delhi NCR, India

Graph Neural Networks Unsupervised Representation Learning Medical Image Classification Graph-Based Semi-Supervised Learning Graph Contrastive Learning Semi-Supervised Learning+1

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In biomedical and neurodegenerative disorders, accurate and early disease identification remains challenging due to the scarcity of labeled data and the complexity of imaging patterns. To address these challenges, we introduce ARMA-C3, a unified unsupervised and semi-supervised graph learning framework for node classification based on contrastive learning and graph-cut regularization to learn structurally meaningful and discriminative representations. By modeling samples or images as graph nodes and exploiting inter-sample relationships, the proposed framework captures subject-level dependencies that conventional machine learning methods typically overlook. We conduct extensive binary classification experiments across five clinically relevant datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Neuroimaging in Frontotemporal Dementia (NIFD) dataset, and three medical imaging benchmarks (BreastMNIST, PneumoniaMNIST, and a liver ultrasound dataset). Experimental results demonstrate that ARMA-C3 achieves competitive and frequently superior performance compared to classical clustering techniques, state-of-the-art machine learning models, and existing graph-based deep learning approaches across multiple evaluation settings, particularly under limited supervision and severe class imbalance. The proposed framework further demonstrates robust representation learning and strong cross-modal generalization across diverse biomedical imaging modalities.