SGAT: simplicial Graph attention network

• Main Authors: Lee, See Hian, Ji, Feng, Tay, Wee Peng
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Engineering::Electrical and electronic engineering::Electronic systems::Signal processing; Graph Neural Networks; Neural Networks; Heterogeneous Graph
• Online Access: https://hdl.handle.net/10356/159273; https://www.ijcai.org/proceedings/2022/
• Institution: Nanyang Technological University

Heterogeneous graphs have multiple node and edge types and are semantically richer than homogeneous graphs. To learn such complex semantics, many graph neural network approaches for heterogeneous graphs use metapaths to capture multi-hop interactions between nodes. Typically, features from non-targe...