Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs

• Main Authors: YU, Xingtong, LIU, Zhenghao, FANG, Yuan, et al.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Few-shot learning; Fine tuning; Graph mining; Graph neural networks; Metalearning; Pre-training; Prompting; Representation learning; Task analysis; Tuning; Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/9703; https://ink.library.smu.edu.sg/context/sis_research/article/10703/viewcontent/TKDE24_GeneralizedGraphPrompt.pdf
• Institution: Singapore Management University

Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised settin...