Graphprompt: Unifying pre-training and downstream tasks for graph neural networks

• Main Authors: LIU, Zemin, YU, Xingtong, FANG, Yuan, ZHANG, Xinming
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Graph neural networks; pre-training; prompt; few-shot learning; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/8191; https://ink.library.smu.edu.sg/context/sis_research/article/9194/viewcontent/TheWebConf23_GraphPrompt.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...