Graph Foundation Models

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9 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
Jun 30, 2026Zekai Chen, Kairui Yang, Xuaner Chen +4Multimodal Federated Learning Representation Learning
Beijing Institute of Technology, Beijing, China
A Fair Evaluation of Graph Foundation Models for Node Property Prediction
Jun 23, 2026Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev +1Graph Neural Networks Graph Foundation Models
1HSE University · 2Yandex Research
Adaptive Recurrent Message Passing for Test Time Computing on Graphs
Jun 21, 2026Junshu Sun, Wanxing Chang, Qingming Huang +1Adaptive Inference Graph Neural Networks
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · DAMO Academy, Alibaba Group, Hangzhou, China.
Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models
Jun 16, 2026Cosimo Gregucci, Obaidah Theeb, Daniel Hernandez +2Cross-Domain Generalization Graph Foundation Models
Institute for AI, University of Stuttgart · University of Edinburgh · University of Southampton
Handling Feature Heterogeneity with Learnable Graph Patches
Jun 16, 2026Yifei Sun, Yang Yang, Xiao Feng +4Graph Representation Learning Cross-Domain Transfer Learning
Zhejiang University Hangzhou, China · Huazhong University of Science and Technology Wuhan, China · Finvolution Group Shanghai, China
Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks
Jun 6, 2026Michał Czuba, Mateusz Stolarski, Adam Piróg +2Dynamical Systems Graph Foundation Models
Department of Artificial Intelligence, Wrocław University of Science and Technology, Wrocław, Poland · Semantics, Warsaw, Poland
The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Jun 4, 2026Shuo Wang, Xiangyu Wang, Quanxin Wang +9Graph Neural Networks Graph Representation Learning
University of Electronic Science and Technology of China · Tsinghua University · Western University +1
Harnessing Structural Context for Entity Alignment Foundation Models
Jun 4, 2026Xingyu Chen, Yuanning Cui, Zequn Sun +1KG Embedding Cross-Domain Transfer Learning
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Nanjing University of Information Science and Technology, Nanjing, China · National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation
Jun 2, 2026Ankang Yang, Jitao Zhao, Dongxiao He +3Graph Neural Networks Cross-Domain Generalization
School of Computer Science and Technology Tianjin University Tianjin, China · School of Artificial Intelligence Hebei University of Technology Tianjin, China · Department of Health Technology and Informatics Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Kowloon, Hong Kong
Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation
Jun 2, 2026Yifan Jin, Qirui Ji, Bin Qin +4Retrieval-Augmented Generation Graph Retrieval-Augmented Generation
Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Beijing, China +1
Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
Jun 2, 2026Yancheng Chen, Dun Ma, Shuai Zhang +6Prompt Tuning Graph Foundation Models
Academy of Mathematics and Systems Science, Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · Institute of Information Engineering, Chinese Academy of Sciences +4
Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
Jun 2, 2026Li Sun, Zhenhao Huang, Yiding Wang +3GNN Expressivity Geometric Representation Learning
Beijing University of Posts and Telecommunications, Beijing, China · North China Electric Power University, Beijing, China · University of Cambridge, Cambridge, UK +1
GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
May 29, 2026Saku Peltonen, August Bøgh Rønberg, Andreas Plesner +1LLM Reasoning with Graphs Synthetic Benchmark Generation
ETH Zürich Zürich, Switzerland
When Do Graph Foundation Models Transfer? A Data-Centric Theory
May 28, 2026Jiajun Zhu, Ying Chen, Peihao Wang +4Graph Representation Learning Cross-Domain Transfer Learning
University of Texas at Austin · Arizona State University · Georgia Institute of Technology
Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling
May 26, 2026Yinan Liu, Wenjin Xu, Zhiyuan Zha +2KG Embedding Graph Foundation Models
Northeastern University Shenyang, China
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
May 25, 2026Ziyu Zheng, Yaming Yang, Ziyu Guan +2Cross-Domain Generalization Cross-Domain Transfer Learning
School of Computer Science and Technology, Xidian University, Xi’an, China · School of Artificial Intelligence, Xidian University, Xi’an, China
Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
May 22, 2026Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim +1Neural Surrogate Modeling Heterogeneous GNNs
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA
S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
May 18, 2026Yuhan Wang, Haopeng Zhang, Yibo Ding +6Cross-Modal Alignment Text-Attributed Graph Learning
Beihang University, Beijing, China · Tianjin University, Tianjin, China
Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
May 15, 2026Jingcheng Wu, Ratan Bahadur Thapa, Mojtaba Nayyeri +4Relational Foundation Models Tabular Foundation Models
University of Stuttgart, Stuttgart, Germany · Web and Internet Science Research Group, University of Southampton, Southampton, United Kingdom
Controllable Molecular Generative Foundation Models
May 14, 2026Yihan Zhu, Yuhan Liu, Weijiang Li +2Molecular Optimization Molecular Graph Generation
University of Notre Dame
KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning
May 14, 2026Yisen Gao, Jiaxin Bai, Haoyu Huang +5Graph Neural Networks In-Context Learning
Department of Computer Science and Engineering, HKUST, Hong Kong, China · Department of Computer Science and Engineering, HKBU, Hong Kong, China
GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning
May 14, 2026Yan Jiang, Ruihong Qiu, Zi HuangDomain Adaptation Prompt Tuning
School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Queensland, Australia
Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
May 13, 2026Haonan Yuan, Qingyun Sun, Junhua Shi +3Domain-Adaptive Pretraining Prompt Tuning
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
Structure-Centric Graph Foundation Model via Geometric Bases
May 9, 2026Xiaodong He, Haolan He, Ruiyi Fang +2Geometric Representation Learning Graph Foundation Models
University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China · Western University
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
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
May 5, 2026Zhongjian Zhang, Yue Yu, Mengmei Zhang +3LLM Evaluation LLM Reasoning with Graphs
Beijing University of Posts and Telecommunications · China Telecom Bestpay · Beihang University
Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment
May 1, 2026Ziyu Zheng, Yaming Yang, Zhe Wang +2Heterogeneous GNNs Cross-Domain Transfer Learning
School of Computer Science and Technology, Xidian University, Xi’an, China 710071
Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
Apr 23, 2026João Mattos, Arlei SilvaGraph Representation Learning Meta-Learning
Computer Science Department · Ken Kennedy Institute

Jun 30, 2026, cs.LGJ/K move · Enter open · S save

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning

Zekai Chen, Kairui Yang, Xuaner Chen, Xunkai Li+3

Beijing Institute of Technology, Beijing, China

Multimodal Federated Learning Representation Learning Federated Graph Learning Graph Foundation Models Multimodal Foundation Models Multimodal Model Interpretability

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Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby supporting diverse graph-centric and modality-centric tasks. In practice, however, such multimodal graphs are often distributed across decentralized clients, where raw contents and local structures cannot be centrally shared due to privacy constraints. This motivates federated multimodal graph foundation learning, which requires not only transferable representation learning but also intrinsic semantic traceability under strict data isolation. Existing methods usually exchange or store knowledge through parameters, prototypes, embeddings, or compact codebooks, which support optimization and transfer but do not explicitly expose how modality evidence, node semantics, and topology context jointly support predictions. To bridge this gap, we propose FedLAB, a traceable semantic codebook framework that organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context. FedLAB further refines these trace units through federated semantic barycenter pre-training while keeping raw multimodal contents and graph structures local. Extensive experiments on 10 benchmarks and 6 downstream tasks show that FedLAB improves over state-of-the-art baselines by up to 7.53%, while preserving a native semantic trace interface.

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