Joint hyperbolic and Euclidean geometry contrastive graph neural networks

• Main Authors: XU, Xiaoyu, PANG, Guansong, WU, Di, SHANG, Mingsheng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Graph neural networks; Hyperbolic embedding; Contrastive learning; Graph representation learning; Databases and Information Systems; Graphics and Human Computer Interfaces; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/7564; https://ink.library.smu.edu.sg/context/sis_research/article/8567/viewcontent/Joint_Hyperbolic_and_Euclidean_Geometry_Contrastive_Graph_Neural_Networks_revision_version.pdf
• Institution: Singapore Management University

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in a wide variety of analytical tasks. Current GNN approaches focus on learning representations in a Euclidean space, which are effective in capturing non-tree-like structural relations, but they fail to model complex relati...