In the evolving landscape of machine learning, the adaptation of pre-trained models through prompt tuning has become increasingly prominent. This trend is particularly observable in the graph domain, where diverse pre-training strategies present unique challenges in developing effective prompt-based tuning methods for graph neural networks. Previous approaches have been limited, focusing on specialized prompting functions tailored to models with edge prediction pre-training tasks. These methods, however, suffer from a lack of generalizability across different pre-training strategies. Recently, a simple prompt tuning method has been designed for any pre-training strategy, functioning within the input graph's feature space. This allows it to theoretically emulate any type of prompting function, thereby significantly increasing its versatility for a range of downstream applications. Nevertheless, the capacity of such simple prompts to fully grasp the complex contexts found in graphs remains an open question, necessitating further investigation. Addressing this challenge, our work introduces the Subgraph-level Universal Prompt Tuning (SUPT) approach, focusing on the detailed context within subgraphs. In SUPT, prompt features are assigned at the subgraph-level, preserving the method's universal capability. This requires extremely fewer tuning parameters than fine-tuning-based methods, outperforming them in 42 out of 45 full-shot scenario experiments with an average improvement of over 2.5%. In few-shot scenarios, it excels in 41 out of 45 experiments, achieving an average performance increase of more than 6.6%.
PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs
School of Artificial Intelligence and Big Data, Hefei University（合肥大学人工智能与大数据学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）
MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
School of Computer Science（计算机科学学院）；Technology, Xidian University, Xi’an, China（技术学院，西安电子科技大学）；School of Artificial Intelligence, Xidian University, Xi’an, China（人工智能学院，西安电子科技大学）
UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction
Leiden University Medical Center（莱顿大学医学中心）；Cardiologs；Philips（飞利浦）；Eindhoven University of Technology（埃因霍温理工大学）；Philips Innovative Technologies（飞利浦创新技术）
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）；School of Economics and Management, Beijing University of Posts and Telecommunications（北京邮电大学经济管理学院）；Department of Data Science, George Washington University（乔治华盛顿大学数据科学系）
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；CPII under InnoHK（创新香港科技促进会）；MThreads AI
