Graph Representation Learning

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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.

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

A Closer Look at the Application of Causal Inference in Graph Representation Learning
Apr 10, 2026Hang Gao, Kunyu Li, Huang Hong +2Graph Representation Learning Causal Inference
Institute of Software Chinese Academy of Science
R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII
Apr 9, 2026Zewei Zhou, Jiajun Zou, Jiajia Zhang +8Electronic Design Automation Graph Neural Networks
Nanjing University of Science and Technology · The Chinese University of Hong Kong
Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
Apr 9, 2026Tong Duy Son, Kohta Sugiura, Marc Brughmans +7Graph Representation Learning Physics-Informed GNNs
The authors are with Siemens Digital Industries Software, Leuven, Belgium.
Toward a universal foundation model for graph-structured data
Apr 7, 2026Sakib Mostafa, Lei Xing, Md. Tauhidul IslamGraph Transfer Learning Graph Representation Learning
Department of Radiation Oncology, Stanford University, Stanford, CA, USA · Institute of Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA · Department of Electrical Engineering, Stanford University, Stanford, CA, USA
Graph Topology Information Enhanced Heterogeneous Graph Representation Learning
Apr 7, 2026He Zhao, Zhiwei Zeng, Yongwei Wang +1Graph Representation Learning Graph Structure Learning
Nanyang Technological University, Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Singapore · Nanyang Technological University, Singapore
Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
Apr 6, 2026MD Shafikul Islam, Mahathir Mohammad Bappy, Saifur Rahman Tushar +1Privacy-Preserving ML Differential Privacy
Mechanical and Industrial Engineering Louisiana State University Baton Rouge, LA 70803 · Department of Computer Science Missouri University of Science and Technology Missouri S&T, Rolla, MO 65409
Learning Superpixel Ensemble and Hierarchy Graphs for Melanoma Detection
Apr 4, 2026Asmaa M. Elwer, Muhammad A. Rushdi, Mahmoud H. AnnabySkin Lesion Classification Medical Image Classification
Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza 12613, Egypt · School of Information Technology, New Giza University, Giza 12256, Egypt · Department of Mathematics, Faculty of Science, Cairo University, Giza 12613, Egypt
CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning
Mar 29, 2026Xuanze Chen, Jiajun Zhou, Yadong Li +2Graph Representation Learning Graph Foundation Models
Xuanze Chen and Yadong Li are with the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China, and also with the Binjiang Cyberspace Security Institute of ZJUT, Hangzhou, 310056, China · Jiajun Zhou, Shanqing Yu and Qi Xuan are with the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China, with the Binjiang Cyberspace Security Institute of ZJUT, Hangzhou, 310056, China, and with the Soovar Technologies Co., Ltd., Hangzhou 310056, China
Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
Mar 29, 2026Tanvir Hossain, Muhammad Ifte Khairul Islam, Lilia Chebbah +2Over-Squashing in GNNs Graph Neural Networks
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA · Department of Data Science and Analytics, Kennesaw State University, 1000 Chastain Road, Kennesaw, GA 30144, USA
Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow
Mar 27, 2026Jicheng Ma, Yunyan Yang, Juan Zhao +1Graph Representation Learning Graph Neural Networks
School of Mathematics, Renmin University of China, Beijing, China. · School of Mathematical Sciences, Key Laboratory of Mathematics and Complex Systems of MOE, Beijing Normal University, Beijing, China.
Causality-Driven Disentangled Representation Learning in Multiplex Graphs
Mar 25, 2026Saba Nasiri, Selin Aviyente, Dorina ThanouGraph Representation Learning Disentangled Representation Learning
EPFL, Lausanne, Switzerland · Department of Electrical and Computer Engineering at Michigan State University, East Lansing, MI
KCLNet: Electrically Equivalence-Oriented Graph Representation Learning for Analog Circuits
Mar 25, 2026Peng Xu, Yapeng Li, Tinghuan Chen +2Representation Learning Graph Representation Learning
Department of Computer Science & Engineering, The Chinese University of Hong Kong · School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)
Can Graph Foundation Models Generalize Over Architecture?
Mar 24, 2026Benjamin Gutteridge, Michael Bronstein, Xiaowen DongLong-Range Graph Learning Graph Foundation Models
University of Oxford · AITHYRA
Riemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligence
Mar 23, 2026Philip S. Yu, Li SunGraph Foundation Models Graph Representation Learning
University of Illinois Chicago, Chicago, USA · Beijing University of Posts and Telecommunications, Beijing, China
Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
Mar 20, 2026Haifang Cao, Yu Wang, Timing Li +2Ollivier-Ricci Curvature Graph Representation Learning
College of Intelligence and Computing, Tianjin University, Tianjin, China
VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation
Mar 19, 2026Chinmay Prabhakar, Bastian Wittmann, Tamaz Amiranashvili +6Graph Generation Graph Representation Learning
University of Zurich, Switzerland · ETH AI Center, Zurich, Switzerland · Technical University of Munich, Germany +1
Node Role-Guided LLMs for Dynamic Graph Clustering
Mar 14, 2026Dongyuan Li, Ying Zhang, Yaozu Wu +1Graph Clustering Dynamic Graph Learning
The University of Tokyo Tokyo, Japan · RIKEN Sendai, Japan
Graph Tokenization for Bridging Graphs and Transformers
Mar 11, 2026Zeyuan Guo, Enmao Diao, Cheng Yang +1Graph Representation Learning Transformer
Beijing University of Posts and Telecom., Beijing 100876, China · DreamSoul
Towards Effective and Efficient Graph Alignment without Supervision
Mar 9, 2026Songyang Chen, Youfang Lin, Yu Liu +2Graph Matching Graph Representation Learning
Beijing Jiaotong University, Beijing, 100044, P.R. China. · Peking University, Beijing, 100871, P.R. China.
Robust Node Affinities via Jaccard-Biased Random Walks and Rank Aggregation
Mar 5, 2026Bastian Pfeifer, Michael G. SchimekGraph Representation Learning
Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria.
Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
Mar 3, 2026Fengzhi Li, Liang Zhang, Yuan Zuo +5LLM Reasoning with Graphs Zero-Shot Learning
JIUTIAN Research, Beijing, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · MIIT Key Laboratory of Data and Decision Intelligence, Beihang University, Beijing, China +1
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
Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger
Feb 28, 2026Li Sun, Ming Zhang, Wenxin Jin +5Hypergraph Neural Networks Graph Representation Learning
Beijing University of Posts and Telecommunications, Beijing, China · North China Electric Power University, Beijing, China · University of Kent, Canterbury, United Kingdom +2
Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions
Feb 28, 2026Yunqing Liu, Yi Zhou, Wenqi FanMolecular Property Prediction Molecular Representation Learning
Department of Computing (COMP), The Hong Kong Polytechnic University · Department of Management and Marketing (MM), The Hong Kong Polytechnic University
Geodesic Semantic Search: Cartographic Navigation of Citation Graphs with Learned Local Riemannian Maps
Feb 27, 2026Brandon Yee, Lucas Wang, Kundana KomminiRiemannian Metric Learning Graph-Based Retrieval
Management Sciences Lab, Yee Collins Research Group
An Analysis of Role Semantics in Directed Dynamic Graph Modeling for Edge Classification
Feb 26, 2026Tyler Bonnet, Marek ReiGraph Representation Learning Self-Supervised Pre-Training
Imperial College London
MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training
Feb 26, 2026Lianze Shan, Jitao Zhao, Dongxiao He +3Graph Representation Learning Heterogeneous GNNs
College of Intelligence and Computing, Tianjin University, Tianjin, China · Departments of Health Technology & Informatics; Data Science & Artificial Intelligence; and Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong
DRESS: A Continuous Framework for Structural Graph Refinement
Feb 24, 2026Eduar Castrillo VelillaGraph Representation Learning
GrapHist: Graph Self-Supervised Learning for Histopathology
Feb 24, 2026Sevda Öğüt, Cédric Vincent-Cuaz, Natalia Dubljevic +4Computational Pathology Graph SSL
LTS4, EPFL, Lausanne, 1015, Vaud, Switzerland
Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction
Feb 23, 2026Jiele Wu, Haozhe Ma, Zhihan Guo +2Molecular Representation Learning Graph Representation Learning
School of Computing, National University of Singapore, Singapore · Computer Science and Engineering, The Chinese University of Hong Kong, China

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A Closer Look at the Application of Causal Inference in Graph Representation Learning

Hang Gao, Kunyu Li, Huang Hong, Baoquan Cui+1

Institute of Software Chinese Academy of Science

Graph Representation Learning Causal Inference

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Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods from causal inference to identify causal subgraphs or mitigate confounders. However, due to the inherent complexity of graph-structured data, these approaches frequently aggregate diverse graph elements into single causal variables, an operation that risks violating the core assumptions of causal inference. In this work, we prove that such aggregation compromises causal validity. Building on this conclusion, we propose a theoretical model grounded in the smallest indivisible units of graph data to ensure that the causal validity is guaranteed. With this model, we further analyze the costs of achieving precise causal modeling in graph representation learning and identify the conditions under which the problem can be simplified. To empirically support our theory, we construct a controllable synthetic dataset that reflects realworld causal structures and conduct extensive experiments for validation. Finally, we develop a causal modeling enhancement module that can be seamlessly integrated into existing graph learning pipelines, and we demonstrate its effectiveness through comprehensive comparative experiments.