Graph In-Context Learning, with the ability to adapt pre-trained graph models to novel and diverse downstream graphs without updating any parameters, has gained much attention in the community. The key to graph in-context learning is to perform downstream graphs conditioned on chosen prompt examples. Existing methods randomly select subgraphs or edges as prompts, leading to noisy graph prompts and inferior model performance. Additionally, due to the gap between pre-training and testing graphs, when the number of classes in the testing graphs is much greater than that in the training, the in-context learning ability will also significantly deteriorate. To tackle the aforementioned challenges, we develop a multi-stage adaptive prompt optimization method GraphPrompter, which optimizes the entire process of generating, selecting, and using graph prompts for better in-context learning capabilities. Firstly, Prompt Generator introduces a reconstruction layer to highlight the most informative edges and reduce irrelevant noise for graph prompt construction. Furthermore, in the selection stage, Prompt Selector employs the $k$-nearest neighbors algorithm and pre-trained selection layers to dynamically choose appropriate samples and minimize the influence of irrelevant prompts. Finally, we leverage a Prompt Augmenter with a cache replacement strategy to enhance the generalization capability of the pre-trained model on new datasets. Extensive experiments show that GraphPrompter effectively enhances the in-context learning ability of graph models. On average across all the settings, our approach surpasses the state-of-the-art baselines by over 8%. Our code is released at https://github.com/karin0018/GraphPrompter.
Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning
College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture（北京建筑大学电气与信息工程学院）；College of Cyber Security of Jinan University（济南大学网络安全学院）；School of Computer, Sun Yat-sen University（中山大学计算机学院）；Guangdong Key Laboratory of Data Security and Privacy Preserving（广东省数据安全与隐私保护重点实验室）；Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（人工智能 Mohamed Bin Zayed 大学机器学习部门）
GAP-Prompt: Gated Adaptive Prompting for Efficient Continual Learning
Université d’Orléans（奥尔良大学）；INSA CVL（中央卢瓦尔河谷国立应用科学学院）；CY Cergy Paris University（塞尔吉-蓬图瓦兹大学）；ENSEA（法国高等电子与数字学院）；CNRS（法国国家科学研究中心）；Université de Technologie de Troyes（特鲁瓦技术大学）
PRO-VPT: Distribution-Adaptive Visual Prompt Tuning via Prompt Relocation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；School of Informatics, Xiamen University（厦门大学信息学院）；Shenzhen University（深圳大学）；Guangdong University of Technology（广东技术大学）；Hong Kong Baptist University（香港 Baptist 大学）；CAIR, HKISI, CAS（中国科学院CAS HKISI CAIR）；MAIS, CASIA（中国科学院CASIA MAIS）
ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, USTC（中国科学技术大学苏州高等研究院）；Hong Kong University of Science and Technology（香港科技大学）；JD Intelligent Cities Research（京东智慧城市研究院）
GaussEdit: Adaptive 3D Scene Editing with Text and Image Prompts
School of Computer and Data Engineering, NingboTech University（计算机与数据工程学院，宁波科技学院）；Ningbo Institute, Zhejiang University（浙江大学宁波学院）；School of Software Technology, Zhejiang University（软件技术学院，浙江大学）；School of Big Data and Artificial Intelligence Management, Xi’an Jiaotong University（大数据与人工智能管理学院，西安交通大学）；School of Computer Science and Technology, ShanDong University（计算机科学与技术学院，山东大学）；Graphics & Geometric Computing Laboratory, School of Mathematical Sciences, University of Science and Technology of China（图形与几何计算实验室，数学科学学院，中国科学技术大学）
