Graph Representation Learning

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Overview

Graph representation learning studies how to turn relational data into useful representations while preserving information about connectivity, node and edge attributes, and sometimes time or geometry. Common approaches include message-passing graph neural networks, spectral and positional encodings, graph Transformers, and self-supervised methods such as contrastive learning and masked prediction. These representations support tasks including node classification, link prediction, graph comparison, clustering, and molecular or social prediction; newer work also explores higher-order structure and foundation models that transfer across graphs. A central challenge is balancing expressive, scalable models with reliable generalization across graph sizes, domains, feature types, and structural shifts, while establishing when graph structure helps beyond simpler baselines.

Latest papers

CardsList

305 papers33 papers in the last four weeks, up 3% on the four weeks before. 0.2% of all new papers.

  1. AdNGCL: Adaptive Negative Scheduling for Graph Contrastive Learning

    May 4, 2026Adnan Ali, Jinlong Li, Syed Muhammad Israr +3Graph Representation LearningGraph SSL

    School of Computer Science and Technology, University of Science and Technology of China, Hefei, China · Hainan University, Haikou, Hainan, China · Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom

  2. PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

    May 2, 2026Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son HyGraph Representation LearningHierarchical Representation Learning

    University of Alabama at Birmingham Birmingham, Alabama, USA

  3. GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning

    May 2, 2026Chuang Liu, Zelin Yao, Xueqi Ma +4Graph Representation LearningSelf-Supervised Pre-Training

    Sangfor Technologies Inc. · Wuhan University · The University of Melbourne +2

  4. Aitchison Embeddings for Learning Compositional Graph Representations

    May 1, 2026Nikolaos Nakis, Chrysoula Kosma, Panagiotis Promponas +2Neural Representation GeometryGraph Representation Learning

    Human Nature Lab, Yale University, New Haven, USA · Universit´e Paris Saclay, Universit´e Paris Cit´e, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France · Yale Institute for Network Science, Yale University, New Haven, USA +2

  5. LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis

    Apr 30, 2026Lincan Li, Zheng Chen, Yushun DongGraph Structure LearningElectroencephalography

    Department of Computer Science, Florida State University · 2SANKEN, The University of Osaka

  6. A Unified Framework of Hyperbolic Graph Representation Learning Methods

    Apr 30, 2026Sofía Pérez Casulo, Marcelo Fiori, Bernardo Marenco +1Graph Representation LearningGraph Embedding

    Facultad de Ingenier´ıa, Universidad de la Rep´ublica, Uruguay · Centro Interdisciplinario en Ciencia de Datos y Aprendizaje Autom´atico (CICADA), Universidad de la Rep´ublica, Uruguay

  7. Learning-Based Hierarchical Scene Graph Matching for Robot Localization Leveraging Prior Maps

    Apr 30, 2026Nimrod Millenium Ndulue, Jose Andres Millan-Romera, Matteo Giorgi +2Simultaneous Localization and MappingGraph Representation Learning

    Authors are with the Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg. Holger Voos is also associated with the Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg. · Author is with the University of Pisa.

  8. Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach

    Apr 30, 2026Yihan Zhang, Ercan E. KuruogluGraph Structure LearningGraph Representation Learning

    Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

  9. TypeBandit: Type-Level Context Allocation and Reweighting for Effective Attribute Completion in Heterogeneous Graph Neural Networks

    Apr 30, 2026Ta-Yang Wang, Rajgopal Kannan, Viktor PrasannaGraph Representation LearningHeterogeneous GNNs

    University of Southern California, Los Angeles, USA · DEVCOM Army Research Office, Los Angeles, USA

  10. Cheeger--Hodge Contrastive Learning for Structurally Robust Graph Representation Learning

    Apr 29, 2026Mengyang Zhao, Longlong Li, Cunquan QuGraph Representation LearningGraph Contrastive Learning

    School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore · Data Science Institute, Shandong University, Jinan 250100, China

  11. Mini-Batch Class Composition Bias in Link Prediction

    Apr 28, 2026Kieran Maguire, Srinandan DasmahapatraGraph Representation LearningLink Prediction

    University of Southampton School of Electronics and Computer Science

  12. Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions

    Apr 27, 2026Qinhan Hou, Jing TangDynamical SystemsGraph 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

  13. Time-varying Interaction Graph ODE for Dynamic Graph Representation Learning

    Apr 27, 2026Xiaoyi Wang, Zhiqiang Wang, Jianqing Liang +4Temporal GNNsGraph Representation Learning

    Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China. · Department of System Engineering and Engineering Management, City University of Hong Kong, Kowloon 999077, Hong Kong, China. · Key Laboratory of Complex Systems and Data Science of Ministry of Education, Complex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China.

  14. Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs

    Apr 26, 2026Jonathan Hoss, Moritz Link, Noah KlarmannGraph Neural NetworksReinforcement Learning

    Faculty of Management and Engineering, Rosenheim Technical University of Applied Sciences, Germany

  15. 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 SystemsGraph 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

  16. Path-Based Gradient Boosting for Graph-Level Prediction

    Apr 26, 2026Claudio Meggio, Johan Pensar, Riccardo De BinGradient-Boosted Decision TreesGraph Representation Learning

    Department of Mathematics, University of Oslo, Oslo, Norway

  17. Layer Embedding Deep Fusion Graph Neural Network

    Apr 25, 2026Taihua Xu, Genhao Tian, Jicong Fan +3Graph Structure LearningGraph Neural Networks

    Jiangsu University of Science and Technology · The Chinese University of Hong Kong, Shenzhen · Chongqing University of Post and Telecommunications

  18. Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning

    Apr 23, 2026João Mattos, Arlei SilvaGraph Representation LearningMeta-Learning

    Computer Science Department · Ken Kennedy Institute

  19. GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion

    Apr 23, 2026Qizhuo Xie, Yunhui Liu, Yu Xing +4Graph Representation LearningKG Completion

    State Key Laboratory for Novel Software Technology, Nanjing University, China

  20. Drug Synergy Prediction via Residual Graph Isomorphism Networks and Attention Mechanisms

    Apr 23, 2026Jiyan Song, Wenyang Wang, Chengcheng Yan +2Graph Neural NetworksGraph Representation Learning

    College of Sciences, Shihezi University, 221 North 4th Road, Shihezi, 832003, Xinjiang, China · School of Mathematics and Computational Science, Xiangtan University, North 2nd Ring Road, Xiangtan, 411105, Hunan, China

  21. When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift

    Apr 21, 2026Saket MagantiGraph Neural NetworksDistribution Shift

  22. Revisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding

    Apr 21, 2026Gerard Pons, Carlos Escolano, Besim Bilalli +1Graph Representation LearningKG Embedding

    Data-intensive Technologies and Knowledge Systems (DTAK), Department of Service and Information System Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Catalonia, Spain · Intelligent Data Science and Artificial Intelligence Research Center (IDEAI), Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, 08034, Catalonia, Spain

  23. TACENR: Task-Agnostic Contrastive Explanations for Node Representations

    Apr 21, 2026Vasiliki Papanikou, Evaggelia PitouraGraph Representation LearningGNN Explainability

    University of Ioannina, Greece · Archimedes/Athena Research Center, Greece

  24. LoReC: Rethinking Large Language Models for Graph Data Analysis

    Apr 20, 2026Hongyu Zhan, Qixin Wang, Yusen Tan +6Graph Representation LearningLLMs for Graph Learning

    The Hong Kong University of Science and Technology (Guangzhou) · Tongji University · Westlake University +2

  25. Contraction and Hourglass Persistence for Learning on Graphs, Simplices, and Cells

    Apr 19, 2026Mattie Ji, Indradyumna Roy, Vikas GargGraph Neural NetworksGraph Representation Learning

    University of Pennsylvania · Aalto University · Indian Institute of Technology Bombay +1

  26. HSG: Hyperbolic Scene Graph

    Apr 19, 2026Liyang Wang, Zeyu Zhang, Hao TangGraph Representation LearningHierarchical Representation Learning

    School of Computer Science, Peking University

  27. Graph self-supervised learning based on frequency corruption

    Apr 17, 2026Haojie Li, Mengjiao Zhang, Guanfeng Liu +3Graph Neural NetworksGraph Representation Learning

    School of Data Science, Qingdao University of Science and Technology Qingdao, China · School of Information Science and Technology, Qingdao University of Science and Technology Qingdao, China · School of Computing, Macquarie University Sydney, Australia

  28. Graph-Based Fraud Detection with Dual-Path Graph Filtering

    Apr 14, 2026Wei He, Wensheng Gan, Philip S. YuFinancial Fraud DetectionGraph Representation Learning

    School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China · Department of Computer Science, University of Illinois Chicago, Chicago 60607, USA

  29. DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification

    Apr 12, 2026Jian Chen, Yuzhu Hu, Xiaoyan Yuan +6Irregular Time-Series ModelingMultivariate Time Series Classification

    Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China · Shenzhen MSU-BIT University, China · Department of Data Science, City University of Hong Kong, China +1

  30. Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models

    Apr 10, 2026Harry Proshian, Nikita Severin, Sergey Nikolenko +5Self-Supervised LearningContrastive Learning

    Steklov Institute of Mathematics Saint Petersburg, Russia · Independent Researcher Belgrade, Serbia · ISP RAS Steklov Institute of Mathematics Saint Petersburg, Russia +4