Graph-Based Semi-Supervised Learning

Also known as SSL

The field studies how a graph connecting labeled and unlabeled examples can reveal structure that helps classify nodes or data points when labels are scarce. Methods include harmonic-function and label-propagation algorithms, Laplacian regularization, graph neural networks, and pseudo-labeling, sometimes with active querying or learned graph construction. These approaches are used in settings such as citation-network node classification, image and molecular classification, and streaming data, with work also targeting faster solvers and better calibration. A central challenge is that gains depend on graph quality: noisy edges, changing distributions, or weak agreement between neighbors can mislead propagation, while stronger smoothing can erase useful distinctions.

Latest papers

33 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

DualGSL: Prior-Guided Graph Structure Learning with Node-Adaptive Prediction Fusion
Oct 23, 2026Yuxia Sun, Yutszyuk Wong, Huihong Chen +3Graph Structure Learning Node Classification
the College of Information Science and Technology, Jinan University, Guangzhou 510632, China · Jinan University, Guangzhou 510632, China · Sun Yat-sen University +1
When Does the Dependency Graph Help Code-to-Architecture Mapping?
Oct 14, 2026Rakhshanda Jabeen, Morgan Ericsson, Jonas Nordqvist +1Software Engineering Graph-Based Semi-Supervised Learning
Department of Computer Science, Linnaeus University, Växjö, Sweden · Electrolux Professional AB, Ljungby, Sweden · Department of Mathematics, Linnaeus University, Växjö, Sweden
Constructing Structured Decision Sources for Consensus-Based Pseudo-Label Learning
Oct 8, 2026Long WangPseudo-Labeling Ensemble Diversity
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)
Quantum Graph Convolutional Networks: Implementation and Trainability Analysis
Sep 17, 2026Paul San Sebastian Sein, Theodor Iosif, Tilen G. Limbäck-Stokin +2Graph-Based Semi-Supervised Learning Variational Quantum Circuits
Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate-Mondragon, Spain · University of the Basque Country/Euskal Herriko Unibertsitatea-EHU · Quantum Learning Labs, Department of Computer Science, University College London, London, United Kingdom +4
Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning
Sep 13, 2026Itai David, Daphna WeinshallSemi-Supervised Learning Graph-Based Semi-Supervised Learning
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
Geometry-Aware Graph Construction via Adaptive Spectral Bandwidth Control
Sep 3, 2026Ecem Bozkurt, Antonio OrtegaGraph Structure Learning Graph-Based Semi-Supervised Learning
Department of Electrical and Computer Engineering University of Southern California, Los Angeles, CA, USA
Algebraic Multigrid Acceleration for Efficient Label Spreading
Aug 26, 2026Antonia van Betteray, Jonathan Klees, Miriam Schäfers +1Graph-Based Semi-Supervised Learning
Osnabrück University, Institute for Computer Science, Osnabrück, Germany · Ruhr University Bochum, Institute for Computer Science, Bochum, Germany
Fermat Active Laplace Learning for Semi-Supervised Hyperspectral Image Classification
Aug 3, 2026Vutichart Buranasiri, James M. MurphyActive Learning Graph-Based Semi-Supervised Learning
Department of Mathematics Tufts University Medford, MA 02155, USA
Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
Jul 30, 2026Rasmus Tirsgaard, Laurits Fredsgaard, Marisa Wodrich +2Ensemble Learning Graph-Based Semi-Supervised Learning
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark.
No Data Is Not No Risk: Visibility Aware Graph-Based Inference of Business Conduct Risk
Jul 29, 2026Tsuyoshi Iwata, Johannes Laurmaa, Ryohei HisanoGraph Neural Networks Graph-Based Semi-Supervised Learning
RepRisk AG Zurich, Switzerland · The University of Tokyo Tokyo, Japan · The Canon Institute for Global Studies Tokyo, Japan
A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning
Jul 13, 2026Junpeng Wu, Ye YuanGraph Neural Networks Graph-Based Semi-Supervised Learning
College of Computer and Information Science Southwest University Chongqing, China
Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification
Jul 10, 2026Camila Piscioneri Magalhães, Lucas Pascotti ValemGraph Neural Networks Graph-Based Semi-Supervised Learning
Institute of Mathematics and Computer Science (ICMC) University of São Paulo (USP) São Carlos – SP – Brazil
Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization
Jul 8, 2026Adam M. ObermanData Augmentation Graph-Based Semi-Supervised Learning
Department of Mathematics and Statistics, McGill University · Mila, Quebec AI Institute · LawZero
A Near-Linear-Time Solver for Graph $p$ -Laplacian Semi-Supervised Learning via Continuation in $p$
Jul 3, 2026Oren E. LivneGraph-Based Semi-Supervised Learning Semi-Supervised Learning
Pine Birch Analytics, Churchville, PA
SLeDGe: Semi-Supervised Learning on Data Streams with Graph Structure Learning
Jun 19, 2026Heechan Moon, Kijung ShinGraph Structure Learning Graph-Based Semi-Supervised Learning
KAIST, Seoul, Republic of Korea
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
Gaussian Rank-Based Neighborhood Degree for Graph Neural Networks in Image Classification
May 23, 2026Rafael Mendonça Duarte, Jean Roberto Ponciano, Lucas Pascotti ValemGraph Neural Networks Graph-Based Semi-Supervised Learning
Institute of Mathematics and Computer Science (ICMC) University of São Paulo (USP) São Carlos – SP – Brazil
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
Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning
May 8, 2026Yaxin Hou, Jun Ma, Hanyang Li +3Graph-Based Semi-Supervised Learning Pseudo-Labeling
School of Computer Science and Engineering, Southeast University, Nanjing, China. · School of Electrical Engineering, Southeast University, Nanjing, China. · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China.
Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning
May 5, 2026Michal ValkoGraph-Based Semi-Supervised Learning Semi-Supervised Learning
Comenius University, Bratislava · University of Pittsburgh
Online semi-supervised perception: Real-time learning without explicit feedback
Apr 30, 2026Branislav Kveton, Michal Valko, Matthai Phillipose +1Graph-Based Semi-Supervised Learning Semi-Supervised Learning
Intel Labs Santa Clara, CA · Intel Labs Seattle, WA · Department of Computer Science University of Pittsburgh +1
Semi-supervised learning with max-margin graph cuts
Apr 29, 2026Branislav Kveton, Michal Valko, Ali Rahimi +1Graph-Based Semi-Supervised Learning Semi-Supervised Learning
Intel Labs Santa Clara · University of Pittsburgh · Intel Labs Berkeley
Large-scale semi-supervised learning with online spectral graph sparsification
Apr 29, 2026Daniele Calandriello, Alessandro Lazaric, Michal ValkoGraph Sparsification Graph-Based Semi-Supervised Learning
Team SequeL Inria Lille – Nord Europe, France
G-Loss: Graph-Guided Fine-Tuning of Language Models
Apr 28, 2026Aditya Sharma, Vinti Agarwal, Rajesh KumarLLM Fine-Tuning Text Classification
BITS Pilani, India · Bucknell University, USA
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
From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts
Apr 17, 2026Michał Romaszewski, Dominik Kopeć, Michał Cholewa +6Tree Species Classification Graph-Based Semi-Supervised Learning
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland · University of Lodz, Narutowicza 68, 90-136, Lodz, Poland · MGGP Aero, Kaczkowskiego 6, 33-100, Tarnów, Poland +1
DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning
Apr 8, 2026S M Shovan, Arindam Khanda, S M Ferdous +2Graph-Based Label Propagation Graph-Based Semi-Supervised Learning
Missouri University of Science and Technology, Rolla, MO, USA · Pacific Northwest National Laboratory, USA
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
Mar 11, 2026Ruiyao Xu, Kaize DingText-Attributed Graph Learning LLMs for Graph Learning
Department of Statistics and Data Science, Northwestern University
Semi-Supervised Learning on Graphs using Graph Neural Networks
Feb 19, 2026Juntong Chen, Claire Donnat, Olga Klopp +1Graph Neural Networks Graph-Based Semi-Supervised Learning
School of Mathematical Sciences, Xiamen University · Department of Statistics, University of Chicago · ESSEC Business School +1

Oct 23, 2026J/K move · Enter open · S save

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

Yuxia Sun, Yutszyuk Wong, Huihong Chen, Longhao Lv+2

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

Graph Structure Learning Node Classification Graph-Based Semi-Supervised Learning Graph Representation Learning

Open paper AskPDF arXiv

Graph structure learning (GSL) adapts propagation graphs to prediction tasks, but the relative predictive value of feature-derived priors and learned relations can vary across nodes. We propose DualGSL, a prior-guided framework for transductive node classification that learns node-specific weights for combining predictions from separate prior and task-adaptive propagation paths. The prior branch augments a feature graph with virtual class nodes to incorporate training-label information, while the structural branch combines learned affinity graphs with the feature-graph prior. The two branches share encoder parameters while preserving distinct representations and classification logits. A node-adaptive gate fuses the branch logits based on their projected representations, element-wise absolute difference, and prediction confidence. A joint objective integrates fused and branch-specific classification losses with prior-branch class-anchor alignment, coordinating graph construction, representation learning, and prediction fusion. Experiments against seven baselines on the AMG18 malware attribution dataset and the widely used 20News text classification and USPS handwritten-digit recognition benchmarks show that DualGSL achieves the highest cross-dataset average accuracy and Macro-F1 among the evaluated methods. Ablation studies on AMG18 further demonstrate the benefit of combining prior and learned propagation through logit fusion.