Node Classification

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9 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Node-level Graph Neural Architecture Search Framework
Oct 7, 2026Lintao Yanga, Sirui Lia, Yaqing Wang +4Graph Neural Networks Neural 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
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 Models Conformal 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
NodeGround: A Node Classification Benchmark in the Graph Foundation Model Era
Sep 30, 2026Jinmo Lee, Dooho Lee, Minho Jeong +1Graph Foundation Models Node Classification
Nums AI
Propagate, Then Sharpen: Post-Hoc Refinement of Frozen Node Classifiers
Sep 28, 2026Preben Johnsen Bentdal, Nello Blaser, Xue-Cheng TaiGraph-Based Semi-Supervised Learning Classification
Department of Informatics, University of Bergen · Norwegian Research Centre (NORCE)
ICE: Task-Aligned Clifford Latent Fields for Multimodal Graph Foundation Models
Sep 24, 2026Xunkai Li, Xu Wang, Yinlin Zhu +4Graph Foundation Models Multimodal 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
Local Evidence and Geometric Readout Repair in Trained GNNs
Sep 22, 2026Nadi Tomeh, Hugo AttaliGraph Neural Networks Node Classification
Université Sorbonne Paris Nord, CNRS, Laboratoire d’Informatique de Paris Nord, LIPN, F-93430 Villetaneuse, France
PreGS: A Parameter-Transfer-Based Multi-Expert Graph Neural Network for Node Classification
Sep 22, 2026Zhicong Cai, Yinglong Zhang, Xiaoying Hong +2Graph Neural Networks Mixture-of-Experts Models
College of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
Graph Domain Adaptation Does Not End with Representation Learning
Sep 22, 2026Ziqian Liu, Yongxue Xu, Enze Zhang +3Unsupervised Domain Adaptation Graph Representation Learning
Sun Yat-sen University, China · University of Wisconsin–Madison, USA · Jiangsu University, China +2
Provable Guarantees for Spectral Structured Prediction
Sep 16, 2026Violet Zheng, Jean HonorioSpectral Methods Node Classification
CIS, The University of Melbourne
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 Networks Node 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
HOPE: Heterophily-Aware Open-Set Node Classification with Pseudo-Extrapolation
Sep 8, 2026Yumeng Dai, Yue Tan, Yixin Liu +3Graph Neural Networks Node Classification
Xi’an Jiaotong University, Xi’an, China · Griffith University, Brisbane, Australia
IMMENSE: Inductive Multi-perspective User Classification in Social Networks
Aug 5, 2026Francesco Benedetti, Antonio Pellicani, Gianvito Pio +1Social Media Analysis Node Classification
Department of Computer Science, University of Bari, Via Orabona 4, Bari, 70125, Italy · Department of Computer Science, University of Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy · Data Science Laboratory, National Interuniversity Consortium for Informatics (CINI), Via Volturno 58, Roma, 00185, Italy +1
Network Information Enhances Unreliable News Domain Detection
Aug 3, 2026Raphaela Keßler, Roman David Ventzke, Viola Priesemann +1Graph Neural Networks Fake News Detection
University of Konstanz, Germany · MPI for Dynamics and Self-Organization, Germany · University of Göttingen, Germany +2
CoRe-GNN: Multilevel Message passing on Coarsened graphs
Aug 3, 2026Antonin Joly, Nicolas Keriven, Aline RoumyGraph Neural Networks Graph Coarsening
CNRS, IRISA, Rennes, FRANCE · INRIA, Rennes, FRANCE
Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning
Jul 31, 2026Dongxiao He, Jiayu Zhang, Jitao Zhao +2Domain-Adversarial Learning Representation Learning
Tianjin University Tianjin, China
Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
Jul 30, 2026Neelam Akula, Surbhi Kumar, Murat Kantarcioglu +1Graph Neural Networks Link Prediction
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA · Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
The Value of Depth in Message Passing on Sparse Graphs: A Kesten-Stigum Dichotomy
Jul 18, 2026Aseem Raj BaranwalGraph Neural Networks Message Passing Neural Networks
NodeImport: Imbalanced Node Classification with Node Importance Assessment
Jul 15, 2026Nan Chen, Zemin Liu, Bryan Hooi +3Class-Imbalanced Learning Graph Neural Networks
Johns Hopkins University Baltimore, Maryland, USA · Zhejiang University Hangzhou, Zhejiang, China · National University of Singapore Singapore, Singapore +1
Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization
Jul 13, 2026Chengcheng Yan, Qingsong WangGraph Neural Networks Graph Representation Learning
College of Artificial Intelligence, Shaoxing Institute of Technology, Shaoxing, 312000, China. · School of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China.
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
TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
Jun 30, 2026Lingjie Chen, Yuanchen Bei, Haobo Xu +3Masked Diffusion Models Link Prediction
UIUC · VISA
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
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
Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement
Jun 18, 2026Jiaqing Chen, Zidu Yin, Yichao Cai +4Graph Representation Learning Graph Contrastive Learning
School of Information Science and Technology, Yunnan Normal University, Kunming, China · Australian Institute for Machine Learning, Adelaide University, Adelaide, Australia · School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia.
SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes
Jun 17, 2026Antriksh Srivastava, Soumyashree KarGraph Attention Networks Graph Representation Learning
Center of Studies in Resources Engineering · Indian Institute of Technology Bombay · Mumbai, India 400076
LLM Features Can Hurt GNNs: Concatenation Interference on Homophilous Graph Benchmarks
Jun 16, 2026Zhongyuan Wang, Pratyusha VemuriGraph Neural Networks Graph Representation Learning
When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice
Jun 8, 2026Neha Sharma, Ritesh SharmaGraph Neural Networks Node Classification
Department of Computer Science Virginia Commonwealth University · Department of Electrical and Computer Engineering Virginia Commonwealth University
Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs
Jun 5, 2026Takuto Takahashi, Itsuki Nakayama, Takahiro Mitani +3Graph Neural Networks Node Classification
The University of Osaka, 1-5 Yamadaoka, Suita, Osaka, Japan
ALINC: Active Learning for Inductive Node Classification via Graph Sampling
Jun 3, 2026Pascal Plettenberg, Denis Huseljic, André Alcalde +2Active Learning Node Classification
Intelligent Embedded Systems, University of Kassel, 34121 Kassel, Germany · CELUS GmbH, 80339 Munich, Germany · GAIN Group, Institute of Data Science, University of Greifswald, 17489 Greifswald, Germany
GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
Jun 1, 2026Canyixing Cui, Tao Wu, Xingping Xian +3Disentangled Representation Learning Graph Neural Networks
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China · School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, China · Computational Communication Research Center, Beijing Normal University, Zhuhai, China +2
Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
May 28, 2026Qian Chang, Ciprian Doru Giurcaneanu, Runsong Jia +6Temporal GNNs Graph Representation Learning
University of Auckland, Auckland, New Zealand · University of Technology Sydney, Sydney, Australia · Central China Normal University, Wuhan, China
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
May 27, 2026Safal Thapaliya, Jiatan Huang, Chuxu ZhangLLM-Assisted Annotation Text-Attributed Graph Learning
University of Connecticut, USA
Closed-Form Node Classification with Exact Graph Unlearning
May 25, 2026Aditya Gaur, Charu SharmaGraph Representation Learning Privacy-Preserving ML
Machine Learning Lab · IIIT Hyderabad
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
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
Sep 28, 2025Fanlong Zeng, Wensheng Gan, Kangjie Chen +1Class-Imbalanced Learning Graph Neural Networks
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China · School of Cybersecurity, Tianjin University, Tianjin 300354, China · University of Illinois Chicago, Chicago, USA
GraphPFN: A Prior-Data Fitted Graph Foundation Model
Sep 25, 2025Dmitry Eremeev, Oleg Platonov, Gleb Bazhenov +2Synthetic Data Pretraining In-Context Learning
1HSE University · 2Yandex Research
Turning Tabular Foundation Models into Graph Foundation Models
Aug 28, 2025Dmitry Eremeev, Gleb Bazhenov, Oleg Platonov +2Graph Foundation Models Node Classification
HSE University · Yandex Research
HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs
Mar 5, 2025Shujie Li, Yuxia Wu, Yuan Fang +1Text-Attributed Graph Learning Node Classification
Beijing University of Post and Telecommunication · Beijing, China · Singapore Management University +1
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sep 9, 2024Sitao Luan, Qincheng Lu, Chenqing Hua +3Graph Neural Networks Node Classification
Mila - Quebec Artiﬁcial Intelligence Institute · 1McGill University

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Node-level Graph Neural Architecture Search Framework

Lintao Yanga, Sirui Lia, Yaqing Wang, Pietro Liò+3

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

Graph Neural Networks Neural Architecture Search Node Classification

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In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data. Nevertheless, traditional approaches typically apply uniform convolution operations to all nodes, regardless of their varying structural and feature characteristics, which can undermine model performance and result in over-smoothing issues as the number of layers increases. To overcome this limitation, in this work, we propose a \textbf{N}ode-Level \textbf{G}raph \textbf{N}eural \textbf{A}rchitecture \textbf{S}earch (N-GNAS) algorithm. It can automatically choose an appropriate network architecture for each subset of nodes when updating node features. N-GNAS also introduces a contrastive learning loss to separate sample features from different categories and vice versa. In experiments conducted on eight datasets for node and graph classification, our methodology outperforms current leading GNAS techniques and traditional human-designed GNNs. For example, it achieves an accuracy rate of 78.26% on the CiteSeer dataset.

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