Graph Structure Learning

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9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution
May 25, 2026Hongtao Wang, Renchi Yang, Haoran Zheng +1Graph Structure Learning Entity Resolution
Hong Kong Baptist University · Hong Kong, China · Zhejiang University +1
MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
May 24, 2026Da Zhang, Bingyu Li, Zhiyuan Zhao +3Graph Structure Learning Selective SSMs
Northwest Polytechnical University · TeleAI, China Telecom · TeleAI +1
Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge
May 21, 2026Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1Multivariate Time Series Forecasting Graph Structure Learning
IMT School for Advanced Studies Lucca · KU Leuven · University of Chieti-Pescara
Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering
May 21, 2026Zeqiang Xian, Caihui Liu, Yong Zhang +1Graph Structure Learning Clustering
Department of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, Jiangxi, China · Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University, Ganzhou 341000, Jiangxi, China
Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
May 20, 2026Yi Huang, Qingyun Sun, Jia Li +2Graph Structure Learning Graph Neural Networks
SKLCCSE, School of Computer Science and Engineering, Beihang University, Beijing, China · Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China
Exploiting Non-Negativity in DAG Structure Learning
May 19, 2026Samuel Rey, Madeline navarro, Gonzalo MateosGraph Structure Learning Structural Causal Models
Dept. of Signal Theory and Communications, Universidad Rey Juan Carlos, Madrid, Spain · Dept. of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA · Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA
PlantPose: Universal Plant Skeleton Estimation via Tree-constrained Graph Generation
May 18, 2026Xinpeng Liu, Hiroaki Santo, Yosuke Toda +1Agricultural Image Analysis Graph Structure Learning
Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, 5650871, Japan. · Phytometrics, Hamamatsu, Shizuoka, 4350036, Japan. · Institute of Transformative Bio-Molecules, Nagoya University, Nagoya, Aichi, 4648601, Japan.
Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning
May 17, 2026Wei Duan, Junyu Xuan, En Yu +2Graph Structure Learning Information Bottleneck
Australian Artificial Intelligence Institute (AAII), University of Technology Sydney
Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
May 17, 2026Keshu Wu, Sixu Li, Zihao Li +3Graph Structure Learning Incomplete Data Imputation
Texas A&M University · University of Wisconsin-Madison
Informative Graph Structure Learning
May 16, 2026Shen Han, Zhiyao Zhou, Jiawei Chen +6Graph Structure Learning Graph Sparsification
College of Computer Science and Technology, Zhejiang University · Zhejiang University · Hangzhou City University +1
A Resampling-Based Framework for Network Structure Learning in High-Dimensional Data
May 12, 2026Ziwei Huang, Zeyuan Song, Paola Sebastiani +1Graph Structure Learning
Department of Physics, Boston University, Boston, MA · Division of Computational Biomedicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA · Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA +4
SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation
May 12, 2026Luke James Miller, Yugyung LeeGraph Structure Learning Image Segmentation
Department of Computing, Analytics and Mathematics; University of Missouri-Kansas City, Kansas City, United States.
Reconnecting Fragmented Citation Networks with Semantic Augmentation
May 12, 2026Vu Thi Huong, Annika Buchholz, Imene Khebouri +5Graph Structure Learning Graph Clustering
Digital Data and Information for Society, Science, and Culture, Zuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany · Institute of Mathematics, Vietnam Academy of Science and Technology, 10072 Hanoi, Vietnam · Software and Algorithms for Discrete Optimization, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany +2
Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data
May 12, 2026Jie Chen, Yuanbiao Gou, Chuanbin Liu +2Graph Structure Learning Multi-View Learning
College of Computer Science Sichuan University Chengdu, China · School of Economics and Management China University of Petroleum (Beijing) Beijing, China · Law School Sichuan University Chengdu, China +1
Can Graphs Help Vision SSMs See Better?
May 11, 2026Dhruv Parikh, Anvitha Ramachandran, Haoyang Fan +3Graph Structure Learning Graph Neural Networks
USC · UT Austin · DEVCOM ARL Army Research Office, USA
One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
May 11, 2026Mengzhou Gao, Kaiwei Wang, Pengfei JiaoIrregular Time-Series Modeling Graph Structure Learning
School of Cyberspace, Hangzhou Dianzi University.
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
May 10, 2026Riccardo Porcedda, Giuseppe Squillace, Bastian Epping +4Graph Structure Learning Graph Neural Networks
Department of Excellence L’EMbeDS, Sant’Anna School of Advanced Studies, Pisa, Italy · Department of Computer Science, University of Pisa, Italy · IMT School for Advanced Studies, Lucca, Italy +4
Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain
May 8, 2026Minkyoung Kim, Beakcheol JangGraph Structure Learning Graph Neural Networks
Graduate School of Information, Yonsei University
Model-to-Data Distillation for Graph Neural Networks
May 7, 2026Debolina Halder Lina, Arlei SilvaGraph Structure Learning Graph Neural Networks
Department of Computer Science, Rice University, Houston, TX 77005, USA · Department of Computer Science & Ken Kennedy Institute, Rice University, Houston, TX 77005, USA
Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discovery
May 5, 2026Amir Rafe, Subasish DasGraph Structure Learning Bayesian Inference
Texas State University San Marcos, USA
Exact and Approximate Algorithms for Polytree Learning
May 5, 2026Juha Harviainen, Frank Sommer, Manuel SorgeGraph Structure Learning Bayesian Networks
Department of Computer Science, University of Helsinki, Helsinki, Finland · Institute of Computer Science, Friedrich-Schiller Universität Jena, Jena, Germany · Institute of Logic and Computation, TU Wien, Vienna, Austria
SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing
May 4, 2026Genhao Tian, Taihua Xu, Shuyin Xia +3Graph Structure Learning Graph Neural Networks
JiangSu University of Science and Technoloy, Zhenjiang, Jiangsu, China · Chongqing University of Posts and Telecommunications, Chongqing, China · Zunyi Normal University, Zunyi, Guizhou, China
LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis
Apr 30, 2026Lincan Li, Zheng Chen, Yushun DongGraph Structure Learning Electroencephalography
Department of Computer Science, Florida State University · 2SANKEN, The University of Osaka
Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach
Apr 30, 2026Yihan Zhang, Ercan E. KuruogluGraph Structure Learning Graph Representation Learning
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Sparse Graph Learning from Sparse Data via Fiedler Number Maximization
Apr 28, 2026Bahar Oveisgharan, Gene Cheung, Andrew EckfordGraph Structure Learning Spectral Methods
York University, Canada
Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions
Apr 27, 2026Qinhan Hou, Jing TangDynamical Systems Graph Structure Learning
Doctoral Program of Computer Science, University of Helsinki, Helsinki, Finland · Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data
Apr 26, 2026Ru Geng, Panayotis Kevrekidis, Yixian Gao +2Dynamical Systems Graph Structure Learning
School of Mathematics and Statistics, Changchun University, Changchun, 130022, China · Department of Mathematics and Statistics, University of Massachusetts, Amherst, 01003, MA, USA · Department of Physics, University of Massachusetts, Amherst, 01003, MA, USA +2
Layer Embedding Deep Fusion Graph Neural Network
Apr 25, 2026Taihua Xu, Genhao Tian, Jicong Fan +3Graph Structure Learning Graph Neural Networks
Jiangsu University of Science and Technology · The Chinese University of Hong Kong, Shenzhen · Chongqing University of Post and Telecommunications
A Fast Binary Splitting Approach for Non-Adaptive Learning of Erdős--Rényi Graphs
Nov 21, 2025Hoang Ta, Jonathan ScarlettGraph Structure Learning
Department of Computer Science, School of Computing, National University of Singapore (NUS), Singapore 117417 · Department of Computer Science, Hanoi University of Science and Technology, Vietnam · Department of Computer Science, Department of Mathematics, and Institute of Data Science, NUS, Singapore 117417
Design of Experiment for Discovering Directed Mixed Graph
Sep 2, 2025Haijie Xu, Chen ZhangGraph Structure Learning Causal Discovery
Department of Industrial Engineering Tsinghua University

May 25, 2026, cs.CLJ/K move · Enter open · S save

Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution

Hongtao Wang, Renchi Yang, Haoran Zheng, Xiangyu Ke

Hong Kong Baptist University · Hong Kong, China · Zhejiang University+1

Graph Structure Learning Entity Resolution Active Learning

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Dirty entity resolution (ER), which identifies records referring to the same real-world entity from a single, messy dataset, is a fundamental task in data management and mining. However, the dominant blocking-matching-clustering paradigm for ER suffers from critical flaws. Its cascaded, decoupled workflow essentially produces a static, sparse graph plagued by missing edges (due to blocking failures) and noisy links (due to matching errors), causing error propagation and yielding suboptimal clusters, particularly when rigid transitivity is imposed in the clustering. We contend that matching and clustering are fundamentally synergistic, both optimizing for the construction of an ideal entity graph. Building upon this insight, we propose Alper, a unified framework that integrates these steps into an iterative probabilistic label propagation process over a global, evolving graph. Unlike disjoint blocking, Alper refines the graph structure and labels dynamically by adaptively integrating "weak but cheap" signals from graph propagation with "strong but expensive" LLM-based pairwise queries. For higher cost-effectiveness, we formulate the signal selection as a constrained optimization problem maximizing cumulative marginal gain under a query budget, solved via our greedy algorithm with provable theoretical guarantees. Our extensive experiments over eight benchmark datasets demonstrate that Alper is consistently superior to state-of-the-art cascaded pipelines.

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