Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as citation, social, and transaction networks. However, existing methods for learning from these graphs often assume that the distributions of training and testing data are consistent. This assumption leads to significant performance degradation when faced with out-of-distribution (OOD) data. In this paper, we address the challenge of node-level OOD detection in text-attributed graphs, with the goal of maintaining accurate node classification while simultaneously identifying OOD nodes. We propose a novel approach, LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs (LECT), which integrates large language models (LLMs) and energy-based contrastive learning. The proposed method involves generating high-quality OOD samples by leveraging the semantic understanding and contextual knowledge of LLMs to create dependency-aware pseudo-OOD nodes, and applying contrastive learning based on energy functions to distinguish between in-distribution (IND) and OOD nodes. The effectiveness of our method is demonstrated through extensive experiments on six benchmark datasets, where our method consistently outperforms state-of-the-art baselines, achieving both high classification accuracy and robust OOD detection capabilities.
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI
College of Electronic Information and Optical Engineering, Nankai University（南开大学电子信息与光学工程学院）；Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology（天津市光电传感器与传感网络技术重点实验室）；Tianjin First Central Hospital, School of Medicine, Nankai University（南开大学医学院附属天津市第一中心医院）
Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning
Institute of Physical Science and Information Technology（物理科学与信息技术研究院）；School of Mathematical Sciences（数学科学学院）；Qinghai Institute of Science and Technology Information（青海科学技术信息研究院）
A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）山东省计算中心（国家超级计算济南中心）算力网络与信息安全教育部重点实验室）；Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science（山东省计算机网络重点实验室、山东省计算机科学基础研究中心）；Institute of Engineering Medicine, Beijing Institute of Technology（北京理工大学医学工程学院）
Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Xi’an Key Laboratory of Big Data and Intelligent Vision（西安大数据与智能视觉重点实验室）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University（教育部协同智能系统重点实验室）；Warren Alpert Medical School, Brown University（布朗大学沃伦·阿尔佩特医学学院）
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Kuaishou Technology（快手科技）
Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
Chongqing Key Laboratory of Computational Intelligence（重庆计算智能重点实验室）；Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education（教育部网络空间大数据智能安全重点实验室）；Key Laboratory of Big Data Intelligent Computing（大数据智能计算重点实验室）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间安全学院）；School of Computer Science and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）
Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design
Department of Computer Science, University of North Carolina at Chapel Hill（北卡罗来纳大学 Chapel Hill 分校计算机科学系）；Radiation Medicine Centre, Bhabha Atomic Research Centre（博哈原子研究中心放射医学中心）；Homi Bhabha National Institute（霍米·巴布纳国家研究所）；IITB-Monash Research Academy, IIT Bombay（印度理工学院 Bombay 的 IITB-Monash 研究学院）；Department of Chemistry, Indian Institute of Technology Bombay（印度理工学院 Bombay 化学系）
An Enhanced Dual Transformer Contrastive Network for Multimodal Sentiment Analysis
Ho Chi Minh City Open University, Ho Chi Minh, Vietnam Insight Centre for Data Analytics \& School of Computing, Dublin City University, Dublin, Ireland
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；Department of Computer Science, Tongji University（同济大学计算机科学系）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）
External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection
School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；Key Laboratory of Computer Network and Information of Ministry of Education of China（中国教育部计算机网络与信息重点实验室）；Purple Mountain Laboratories（紫金山实验室）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education（教育部区块链应用监管与管理工程研究中心（东南大学））
针对多模态假新闻检测中信息利用不充分及外部信息静态化的问题，提出外部可靠信息增强多模态对比学习框架 ERIC-FND，通过实体增强、多模态对比学习与自适应融合提升检测效果，在 X 和 Weibo 数据集上优于现有方法。
UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding
New York University Shanghai（上海纽约大学）；Bakar Computational Health Sciences Institute（贝克计算健康科学研究所）；University of California San Francisco（加利福尼亚大学旧金山分校）；University of Georgia（佐治亚大学）
Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation
Department of Computer Science and Engineering（计算机科学与工程系）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry（牙科学院儿童牙科与正畸科）；The University of Hong Kong（香港大学）；Delun Dental Hospital（德尔伦牙科医院）；Department of Chemical and Biological Engineering（化学与生物工程系）；Division of Life Science（生命科学系）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）；State Key Laboratory of Nervous System Disorders（神经系统紊乱国家重点实验室）
University of Hong Kong（香港大学）；South China University of Technology（华南理工大学）；Wilfrid Laurier University（威尔弗里德·劳雷尔大学）；Baidu Inc.（百度公司）；York University（约克大学）
HC$^2$L: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding
University of Science and Technology Beijing（北京科技大学）；Agency for Science, Technology and Research（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）；University of Technology Sydney（悉尼科技大学）
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning
University of Technology Sydney（悉尼科技大学）；The Hong Kong Polytechnic University（香港理工大学）；City University of Hong Kong（香港城市大学）；The Education University of Hong Kong（香港教育大学）
Towards Performance-Enhanced Model-Contrastive Federated Learning using Historical Information in Heterogeneous Scenarios
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences)（计算能力网络与信息安全重点实验室，教育部，山东计算机科学中心，齐鲁工业大学（山东科学院））；School of Information and Software Engineering, University of Electronic Science and Technology of China（信息与软件工程学院，电子科技大学）；School of Computer Science and Engineering, Shandong University of Science and Technology（计算机科学与工程学院，山东科技大学）；School of Big Data and Software Engineering, Chongqing University（大数据与软件工程学院，重庆大学）
UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Jiaotong University（北京交通大学）；Northwestern Polytechnical University（西北工业大学）；Alibaba Group, US（美国阿里巴巴集团）；National University of Singapore（新加坡国立大学）
