Graph foundation models have recently attracted significant attention due to its strong generalizability. Although existing methods resort to language models to learn unified semantic representations across domains, they disregard the unique structural characteristics of graphs from different domains. To address the problem, in this paper, we boost graph foundation model from structural perspective and propose BooG. The model constructs virtual super nodes to unify structural characteristics of graph data from different domains. Specifically, the super nodes fuse the information of anchor nodes and class labels, where each anchor node captures the information of a node or a graph instance to be classified. Instead of using the raw graph structure, we connect super nodes to all nodes within their neighborhood by virtual edges. This new structure allows for effective information aggregation while unifying cross-domain structural characteristics. Additionally, we propose a novel pre-training objective based on contrastive learning, which learns more expressive representations for graph data and generalizes effectively to different domains and downstream tasks. Experimental results on various datasets and tasks demonstrate the superior performance of BooG. We provide our code and data here: https://anonymous.4open.science/r/BooG-EE42/.
Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training
Southeast University（东南大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Case Western Reserve University（凯斯西储大学）；Centre de Recherche en Information Biomédicale Sino-Français（中法生物医学信息研究中心）
McGill University, Department of Mathematics and Statistics（麦吉尔大学数学与统计学系）；Université Marie et Louis Pasteur, CNRS, LmB (UMR 6623)（玛丽·路易斯·帕斯特大学、法国国家科学研究中心、LmB（UMR 6623））
The language of time: a language model perspective on time-series foundation models
College of Computer Science and Artificial Intelligence, Fudan University（计算机科学与人工智能学院，复旦大学）；Shanghai Key Laboratory of Data Science（上海数据科学重点实验室）；ZCTech；School of Mathematics and Statistics, Beijing Institute of Technology（数学与统计学学院，北京理工大学）
Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives
Faculty of Electrical Engineering, Czestochowa University of Technology（电子工程学院，捷斯托霍瓦技术大学）；Faculty of Computer Science and Artificial Intelligence, Czestochowa University of Technology（计算机科学与人工智能学院，捷斯托霍瓦技术大学）
AttnBoost: Retail Supply Chain Sales Insights via Gradient Boosting Perspective
Nanyang Technological University（南洋理工大学）；Washington State University（华盛顿州立大学）；Southern Methodist University（南方 Methodist 大学）；Georgetown University（乔治城大学）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；China Construction Bank（中国建设银行）
OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad
Xiamen University（厦门大学）；Shenzhen University（深圳大学）；The Australian National University（澳大利亚国立大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
University of Technology Nuremberg（纽伦堡工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Alberta（阿尔伯塔大学）；ETH Zürich（苏黎世联邦理工学院）；University of California San Diego（加州大学圣迭戈分校）
Protecting patient privacy in clinical foundation models: Technical and legal perspectives
Massachusetts Institute of Technology (MIT)（麻省理工学院（MIT））；The Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学博德研究所）；Borealis AI；Stanford University（斯坦福大学）；Aalto University（阿尔托大学）；Max Planck Institute for Tax Law and Public Finance（马克斯·普朗克税法与公共财政研究所）；Stanford Law School（斯坦福法学院）；Harvard Law School（哈佛法学院）；Petrie-Flom Center for Health Law Policy, Biotechnology & Bioethics（皮里-弗洛姆健康法律政策、生物技术与生物伦理中心）
Configurable Foundation Models: Building LLMs from a Modular Perspective
Tsinghua University（清华大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Carnegie Mellon University（卡内基梅隆大学）；ModelBest Inc.（模百科技）；Princeton University（普林斯顿大学）；National University of Singapore（新加坡国立大学）；Stanford University（斯坦福大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Renmin University of China（中国人民大学）
