Learning to pre-train graph neural networks

• Main Authors: LU, Yuanfu, JIANG, Xunqiang, FANG, Yuan, SHI, Chuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: pre-training; graph neural networks; meta-learning; MAML; Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/6125; https://ink.library.smu.edu.sg/context/sis_research/article/7128/viewcontent/16552_Article_Text_20046_1_2_20210518.pdf
• Institution: Singapore Management University

Graph neural networks (GNNs) have become the de facto standard for representation learning on graphs, which derive effective node representations by recursively aggregating information from graph neighborhoods. While GNNs can be trained from scratch, pre-training GNNs to learn transferable knowledge...