Heterogeneous graph transformer with poly-tokenization

• Main Authors: LU, Zhiyuan, FANG, Yuan, YANG, Cheng, SHI, Chuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Data Mining; Mining heterogenous data; Mining graphs; Graph transformer; Graphic model; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/9678; https://ink.library.smu.edu.sg/context/sis_research/article/10678/viewcontent/IJCAI24_PHGT.pdf
• Institution: Singapore Management University

Graph neural networks have shown widespread success for learning on graphs, but they still face fundamental drawbacks, such as limited expressive power, over-smoothing, and over-squashing. Meanwhile, the transformer architecture offers a potential solution to these issues. However, existing graph tr...