Accelerating large-scale heterogeneous interaction graph embedding learning via importance sampling

• Main Authors: JI, Yugang, YIN, Mingyang, YANG, Hongxia, ZHOU, Jingren, ZHENG, Vincent W., SHI, Chuan, FANG, Yuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Heterogeneous interaction graphs; Large-scale graphs; Type-dependent sampler; Type-fusion sampler; Importance sampling; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5879; https://ink.library.smu.edu.sg/context/sis_research/article/6890/viewcontent/Accelerating_large_scale_pvoa.pdf
• Institution: Singapore Management University

In real-world problems, heterogeneous entities are often related to each other through multiple interactions, forming a Heterogeneous Interaction Graph (HIG in short). While modeling HIGs to deal with fundamental tasks, graph neural networks present an attractive opportunity that can make full use o...