Graph-Based Label Propagation

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16 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Revisiting Simple Baselines for Relational Deep Learning
Oct 14, 2026Viet The Nguyen, Vijay Prakash Dwivedi, Gunther GustGraph-Based Label Propagation Relational Deep Learning
University of Würzburg · Seamless Labs, Inc. · Stanford University
What Egocentric Video Embeddings Encode and How to Combine Them with Inertial Sensors for Human Activity Recognition
Oct 9, 2026Kansuke HabanoGraph-Based Label Propagation Human Activity Recognition
Independent Researcher, Japan
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)
COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shifts
Aug 19, 2026Yanghong Lin, Li Fang, Tianyu Li +2Graph-Based Label Propagation 3D Point Cloud Segmentation
Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350108, Fujian, China. · Spatial Intelligence and Urban Computing, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, Fujian, China. · State Key Lab for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, Fujian, China. +2
SSR: Similarity-Shift Refinement for Training-Free Object-Centric Masks
Aug 2, 2026Xiaoqian Lu, Guangfu GuoGraph-Based Label Propagation Image Segmentation
University of Ottawa · Clemson University
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
Multi-Label Node Classification with Label Influence Propagation
Jul 1, 2026Yifei Sun, Zemin Liu, Bryan Hooi +4Graph-Based Label Propagation Multi-Label Classification
Zhejiang University · National University of Singapore · Capital One +1
MaskedFOP: Polyglot Speaker Identification under Missing Visual Modality via Cascaded Graph Label Propagation
Jun 12, 2026Ayoub Elkhouzari, Youssef Iraqi, Loubna MekouarMissing-Modality Learning Graph-Based Label Propagation
College of Computing, University Mohammed VI Polytechnic
Towards Robust and Scalable Density-based Clustering via Graph Propagation
May 1, 2026Yingtao Zheng, Hugo Phibbs, Ninh PhamGraph-Based Label Propagation Clustering
School of Computer Science, University of Auckland, New Zealand. · Department of Mathematics and Computer Science, University of Southern Denmark, Denmark.
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
MapSR: Prompt-Driven Land Cover Map Super-Resolution via Vision Foundation Models
Apr 16, 2026Ruiqi Wang, Qi Yu, Jie Ma +1Graph-Based Label Propagation Land Cover Classification
School of Information Science and Technology, Beijing Foreign Studies University, Beijing 100089, China
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
PEARL: Geometry Aligns Semantics for Training-Free Open-Vocabulary Semantic Segmentation
Mar 23, 2026Gensheng Pei, Xiruo Jiang, Xinhao Cai +3Graph-Based Label Propagation Open-Vocabulary Semantic Segmentation
Department of Electrical and Computer Engineering, Sungkyunkwan University · School of Computing and Artificial Intelligence, Southwest Jiaotong University · School of Computer Science and Engineering, Nanjing University of Science and Technology
BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop
Mar 2, 2026Chao Chen, Xujia Li, Dongsheng Hong +4Graph-Based Label Propagation Graph Representation Learning
Harbin Institute of Technology (Shenzhen), Guangdong, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China · Fuzhou University, Fujian, China +1
Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size
Oct 6, 2025Farid Bozorgnia, Vyacheslav Kungurtsev, Shirali Kadyrov +1Over-Smoothing in GNNs Graph-Based Semi-Supervised Learning
GLL: A Differentiable Graph Learning Layer for Neural Networks
Dec 11, 2024Jason Brown, Bohan Chen, Harris Hardiman-Mostow +2Graph-Based Label Propagation Graph-Based Semi-Supervised Learning
Department of Mathematics University of California, Los Angeles Los Angeles, CA 90095, USA · Computing + Mathematical Sciences (CMS) Department California Institute of Technology Pasadena, CA 91125, USA · School of Mathematics University of Minnesota Minneapolis, MN 55455, USA

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Revisiting Simple Baselines for Relational Deep Learning

Viet The Nguyen, Vijay Prakash Dwivedi, Gunther Gust

University of Würzburg · Seamless Labs, Inc. · Stanford University

Graph-Based Label Propagation Relational Deep Learning

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Relational deep learning has emerged as a powerful paradigm for learning end-to-end predictive models over relational databases by representing them as temporal heterogeneous graphs. Recent advances based on graph neural networks and Transformers have achieved strong performance across diverse domains. However, the effectiveness of simpler relational baselines remains underexplored, making it difficult to assess how much architectural complexity is needed to achieve strong predictive performance. Motivated by the observation that predictive signals arise from target attributes, relational attributes, and historical labels, we revisit how effectively these signals can be exploited using simple propagation methods. We adapt fixed low-pass feature propagation to relational entity graphs and train an MLP for prediction. The resulting predictions are further refined using Correct-and-Smooth, a graph-based label propagation algorithm that incorporates historical labels over task-specific meta-graphs connecting target entities. Across classification and regression tasks on RelBench, an MLP trained on neighborhood-filtered features outperforms one using only target-entity features on most tasks and recovers a substantial portion of the performance of heterogeneous GNNs. Correct-and-Smooth provides further gains from historical labels, although applying it requires a separately constructed meta-graph because connections among target entities are not directly specified by the relational entity graph. Together, our studies revisit the effectiveness of established propagation methods as simple RDL baselines while identifying canonical meta-graph construction as a key challenge for effective label propagation.