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.

Latest papers

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

Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization
May 12, 2026Yunfei Nie, Jiawei Wang, Ruobing Yan +3Graph Neural Networks Graph Representation Learning
Brandeis University, Waltham, USA · University of California, Los Angeles, USA · Georgetown University, Washington, D.C., USA +2
Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification
May 12, 2026Kamel Abdous, Nairouz Mrabah, Mohamed BouguessaGraph Representation Learning Spectral Methods
Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada
A Unified Perspective for Learning Graph Representations Across Multi-Level Abstractions
May 12, 2026Mohamed Mahmoud Amar, Nairouz Mrabah, Mohamed Bouguessa +1Graph Representation Learning Graph SSL
Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada
From Message-Passing to Linearized Graph Sequence Models
May 12, 2026Joël Mathys, Basil Rohner, Saku Peltonen +1Long-Range Graph Learning Graph Representation Learning
1ETH Zurich, Switzerland
CAD-feature enhanced machine learning for manufacturing effort estimation on sheet metal bending parts
May 12, 2026Matteo Ballegeer, Toon Van Camp, Willem Jaspers +6Manufacturing Graph Representation Learning
A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
May 12, 2026Haibo Chen, Xin Wang, Jiaheng Chao +2LLM Alignment Graph Representation Learning
Tsinghua University
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
May 12, 2026Yingjie Zhou, Yuqin Xie, Fanxing Liu +3Graph Representation Learning Anomaly Detection
Sichuan University, Chengdu 610065, China · University of Connecticut, Storrs, CT 06269, USA · University of Electronic Science and Technology of China, Chengdu 611731, China +1
Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
May 12, 2026Chengcheng Sun, Jiayun Tian, Cheng Zhai +5KG Construction Graph Neural Networks
School of Safety Engineering, China University of Mining and Technology, China · School of Computer Science and Technology, China University of Mining and Technology, China · State Key Laboratory of Coal Mine Disaster Prevention and Control, China University of Mining and Technology, China +1
CORE: Cyclic Orthotope Relation Embedding for Knowledge Graph Completion
May 11, 2026Yingqi Zeng, Luying Wang, Huiling ZhuGraph Representation Learning KG Embedding
Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models
May 11, 2026Nikolaos Nakis, Panagiotis Promponas, Konstantinos Tsirkas +4Representation Geometry Intrinsic Dimensionality
Human Nature Lab, Yale University · Department of Electrical and Computer Engineering, Yale University · Department of Statistics and Data Science, Yale University +1
It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction
May 11, 2026H. Ibrahim Erdogan, Punith Raviswamy, Nikita Agrawal +5Graph Representation Learning Materials Property Prediction
University of Bayreuth, Germany · Faculty of Engineering Science, University of Bayreuth, Germany · Faculty of Mathematics, Physics & Computer Science, University of Bayreuth, Germany +5
Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions
May 11, 2026Sayan Kumar ChakiGraph Representation Learning KG Embedding
Inria, Laboratoire Hubert Curien, Université Jean Monnet
Learning Graph Foundation Models on Riemannian Graph-of-Graphs
May 11, 2026Haokun Liu, Zezhong Ding, Xike XieGraph Representation Learning Domain Generalization
School of Biomedical Engineering, University of Science and Technology of China (USTC), Suzhou, Jiangsu, China · Data Darkness Lab, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, China · School of Artificial Intelligence and Data Science, USTC, Hefei, Anhui, China
Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
May 11, 2026Ziqi Gao, Chenyi Zi, Zijing Liu +3Prompt Learning Graph Representation Learning
Tsinghua University · The Hong Kong University of Science and Technology (Guangzhou) · IDEA Research +1
CTQWformer: A CTQW-based Transformer for Graph Classification
May 10, 2026Zhan Li, Wuqing Yu, Yusen Wu +1Graph Representation Learning Quantum-Inspired ML
school of Artificial Intelligence, Beijing Normal University, Beijing, China
GravityGraphSAGE: Link Prediction in Directed Attributed Graphs
May 10, 2026Riccardo Porcedda, Francesca Chiaromonte, Fabrizio Lillo +1Graph Neural Networks Graph Representation Learning
Department of Excellence L’EMbeDS, Sant’Anna School of Advanced Studies, Pisa, Italy · Department of Statistics and Huck Institutes of the Life Sciences, The Pennsylvania State University, USA · Class of Science, Scuola Normale Superiore, Pisa, Italy +1
Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
May 8, 2026Hyunmog KimGraph Neural Networks Graph Representation Learning
Independent Researcher.
GraphReAct: Reasoning and Acting for Multi-step Graph Inference
May 8, 2026Xingtong Yu, Zhongwei Kuai, Chang Zhou +6Graph Representation Learning Graph-Based Retrieval
The Chinese University of Hong Kong · The University of Science and Technology of China · University of Electronic Science and Technology of China +3
Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
May 8, 2026Qing Qing, Xikun Zhang, Zhongyuan Zhang +7Graph Neural Networks Graph Representation Learning
Jilin University · RMIT University · The Chinese University of Hong Kong +1
GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges
May 8, 2026Jingjing Zhou, Shiyu Huang, Qing Qing +7Graph Neural Networks Graph Representation Learning
Zhejiang Gongshang University · Jilin University · Adelaide University +2
AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning
May 8, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +7Graph Representation Learning Temporal KGs
LG AI Research Seoul, South Korea
Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention
May 7, 2026Tianxin Zhou, Xiang Li, Haibing LuGraph Attention Networks Graph Representation Learning
Department of Computer Science & Engineering, Santa Clara University, Santa Clara, USA · Dept. of Information Systems & Analytics, Santa Clara University, Santa Clara, USA
Edge-specific signal propagation on mature chromophore-region 3D mechanism graphs for fluorescent protein quantum-yield prediction
May 7, 2026Yuchen Xiong, Swee Keong Yeap, Steven Aw Yoong KitGraph Representation Learning Molecular Property Prediction
1China-ASEAN College of Marine Sciences, Xiamen University Malaysia, Sepang 43900, Selangor, Malaysia
On the Safety of Graph Representation Learning
May 7, 2026Xiaoguang Guo, Zehong Wang, Ziming Li +5Graph Representation Learning Algorithmic Fairness
University of Connecticut · University of Notre Dame
Patch-Effect Graph Kernels for LLM Interpretability
May 7, 2026Ruben Fernandez-Boullon, David N. OlivieriGraph Representation Learning Mechanistic Interpretability
Department of Computer Science, University of Vigo, Ourense 32004, Spain
Diversity Curves for Graph Representation Learning
May 7, 2026Katharina Limbeck, Nadja Häusermann, Martin Carrasco +2Graph Representation Learning Graph Coarsening
1Helmholtz Munich · 2Technical University of Munich · 5Université de Fribourg +2
Measuring Structure in Graph Benchmark Datasets Using Graph Invariants
May 7, 2026Richard von Moos, Mathieu Alain, Bastian RieckGraph Neural Networks Graph Representation Learning
University of Fribourg · University College London
Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models
May 7, 2026Kossi Amouzouvi, Robert Wardenga, Jens Lehmann +1Graph Representation Learning Graph Foundation Models
ScaDS.AI Dresden/Leipzig, Technische Universität Dresden, Dresden, Germany · alphaspeech - c/o alpha NT GmbH, Dresden, Germany · Amazon, Technische Universität Dresden, Dresden, Germany +1
Bridging Input Feature Spaces Towards Graph Foundation Models
May 6, 2026Moshe Eliasof, Krishna Sri Ipsit Mantri, Beatrice Bevilacqua +2Graph Representation Learning Transfer Learning
University of Cambridge · Ben-Gurion University of the Negev · Purdue University
Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
May 5, 2026Filip Kronström, Alexander H. Gower, Daniel Brunnsåker +2Graph Neural Networks Representation Learning
Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Sweden · Department of Life Sciences, Chalmers University of Technology, Sweden · Department of Industrial Biotechnology, KTH Royal Institute of Technology, Sweden +1

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Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization

Yunfei Nie, Jiawei Wang, Ruobing Yan, Yuhan Wang+2

Brandeis University, Waltham, USA · University of California, Los Angeles, USA · Georgetown University, Washington, D.C., USA+2

Graph Neural Networks Graph Representation Learning Financial Fraud Detection

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Financial transaction fraud prevention faces challenges such as complex relationship structures, concealed behavioral patterns, and dynamically changing data distribution. Discrimination models relying solely on independent sample features are insufficient to fully characterize the risks of group collaboration and chain transfers within transaction networks. This paper proposes a graph neural network representation learning and risk discrimination framework for financial transaction fraud prevention. It integrates transaction records and identity information into node attributes and constructs a transaction graph based on shared attributes and interaction consistency to explicitly model inter-transaction relationships. In model design, a multi-layer message passing mechanism is employed to aggregate neighborhood information, learn node embedding representations containing structural context semantics, and output transaction-level fraud probability and risk scores through a lightweight risk discrimination head. A weighted supervision objective is introduced to mitigate training bias caused by class imbalance, and structural consistency regularization constraints are combined to suppress the impact of noisy edges on representation drift, thereby improving the stability and usability of risk characterization. Experiments are conducted on a publicly available financial transaction dataset, comparing various methods in the same direction and comprehensively evaluating them under a unified evaluation protocol. The results show that the proposed method outperforms other methods in risk ranking and probability calibration quality, validating the effectiveness of graph structure modeling and representation learning collaboration in financial transaction fraud prevention.