Graph learning has become essential in various domains, including recommendation systems and social network analysis. Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information and improving performance in tasks like link prediction and node classification. However, a key challenge remains: the difficulty of generalizing to unseen graph data with different properties. In this work, we propose a novel graph foundation model, called OpenGraph, to address this challenge. Our approach tackles several technical obstacles. Firstly, we enhance data augmentation using a large language model (LLM) to overcome data scarcity in real-world scenarios. Secondly, we introduce a unified graph tokenizer that enables the model to generalize effectively to diverse graph data, even when encountering unseen properties during training. Thirdly, our developed scalable graph transformer captures node-wise dependencies within the global topological context. Extensive experiments validate the effectiveness of our framework. By adapting OpenGraph to new graph characteristics and comprehending diverse graphs, our approach achieves remarkable zero-shot graph learning performance across various settings. We release the model implementation at https://github.com/HKUDS/OpenGraph.
IOHunter: Graph Foundation Model to Uncover Online Information Operations
ICAR-CNR（意大利国家研究委员会信息与自动化研究所）；University of Pisa（比萨大学）；Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；University of Southern California（南加州大学）；Spotify（Spotify公司）
HeTa: Relation-wise Heterogeneous Graph Foundation Attack Model
School of Cyberspace, Hangzhou Dianzi University（电子科技大学信息学院）；Data Security Governance Zhejiang Engineering Research Center, Hangzhou Dianzi University（浙江省数据安全治理工程研究中心）；Beihang University（北航）
针对异质图神经网络攻击难以跨模型与跨图泛化的问题，提出 HeTa 基础攻击模型，利用共享关系感知攻击单元和逐关系序列化攻击实现可迁移、易适配的强攻击。
Foundations and Frontiers of Graph Learning Theory
University of Science and Technology of China（中国科学技术大学）；Huawei（华为）；Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen University（中山大学）；Peking University（北京大学）
Diffusion-Guided Pretraining for Brain Graph Foundation Models
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA（电气与计算机工程系，莱维大学）；Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA（计算机科学与工程系，莱维大学）；Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学医学院）
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks
Yale University（耶鲁大学）；Brown University（布朗大学）；New York University（纽约大学）；MIT（麻省理工学院）；ETH Zurich（苏黎世联邦理工学院）；Idaho State University（爱达荷州立大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；Baylor College of Medicine（贝勒医学院）；EPFL（洛桑联邦理工学院）；University of Southern California（南加州大学）
Position: Graph Foundation Models are Already Here
Michigan State University（密歇根州立大学）；Université de Montréal（蒙特利尔大学）；Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Snap Inc.（Snap公司）；Intel Labs（英特尔实验室）
Graph Foundation Models: Concepts, Opportunities and Challenges
School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；China Telecom Bestpay（中国电信翼支付）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Lehigh University（利哈伊大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Rutgers University（罗格斯大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；University of Science and Technology of China（中国科学技术大学）；Meta AI；North Carolina State University（北卡罗来纳州立大学）
Coupling Perception and Reasoning in Federated Multimodal Graph Foundation Models
Beijing Institute of Technology（北京理工大学）；Shandong University（山东大学）；Institute of Information Engineering（信息工程研究所）；University of Science and Technology Beijing（北京科技大学）
Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
University of Stuttgart, Stuttgart, Germany（斯图加特大学）；Internet Science Research Group, University of Southampton, Southampton, United Kingdom（互联网科学研究组，南安普顿大学）
University of Notre Dame（诺丁汉大学）；University of Connecticut（康涅狄格大学）；University of Virginia（弗吉尼亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Cambridge（剑桥大学）；Mila - Québec AI Institute（魁北克AI研究院）；Université de Montréal（蒙特利尔大学）
Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm
Faculty of Engineering, The University of Hong Kong（香港大学工程学院）；Costello College of Business, George Mason University（乔治·马歇尔大学商学院）；Faculty of Business and Economics, The University of Hong Kong（香港大学商学院）；College of Engineering, University of California, Berkeley（加州大学伯克利分校工程学院）
Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models
ScaDS.AI Dresden/Leipzig, Technische Universität Dresden（ScaDS.AI 德累斯顿/莱比锡，德累斯顿技术大学）；Department of Mathematics, KNUST（数学系，库马西大学）；alphaspeech - c/o alpha NT GmbH；Amazon, Technische Universität Dresden（亚马逊，德累斯顿技术大学）；TIB – Leibniz Information Centre for Science and Technology（TIB – 莱比锡科学与技术信息中心）
