Metagraph-based learning on heterogeneous graphs

• Main Authors: FANG, Yuan, LIN, Wenqing, ZHENG, Vincent W., WU, Min, SHI, Jiaqi, CHANG, Kevin, LI, Xiao-Li
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Semantic Proximity Search; Meta-structures; Graph Mining; Heterogeneous Graph Representation; Computer Engineering; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/4726; https://ink.library.smu.edu.sg/context/sis_research/article/5729/viewcontent/TKDE19_MG.pdf
• Institution: Singapore Management University

Data in the form of graphs are prevalent, ranging from biological and social networks to citation graphs and the Web. Inparticular, most real-world graphs are heterogeneous, containing objects of multiple types, which present new opportunities for manyproblems on graphs. Consider a typical proximity...