HGPrompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning

• Main Authors: YU, Xingtong, FANG, Yuan, LIU, Zemin, ZHANG, Xinming
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Databases and Information Systems; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/8712; https://ink.library.smu.edu.sg/context/sis_research/article/9715/viewcontent/29596_Article_Text_33650_1_2_20240324.pdf
• Institution: Singapore Management University

Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised framework greatly depends on the availability of task-specific supervision. To redu...