GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications

被引：0
|
作者：
Shen, Huawei [1 ]
Tang, Jian [2 ]
Bao, Peng [3 ]
机构：
[1] Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China
[2] HEC Montreal Montreal Inst Learning, Montreal, PQ, Canada
[3] Beijing Jiaotong Univ, Sch Software Engn, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) | 2019年
基金：
中国国家自然科学基金;
关键词：
Representation learning; Graph neural network; Applications;
D O I：
10.1145/3357384.3358803
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Graphs are the universal data structures for representing the relationships between interconnected objects. They are ubiquitous in a variety of disciplines and domains ranging from computer science, social science, economics, medicine, to bioinformatics. In Recent years, extensive studies have been conducted on the graph analysis techniques. One of the most fundamental challenges of analyzing graphs is effectively representing graphs, which largely determines the performance of many follow-up tasks. This workshop aims to provide a forum for industry and academia to discuss the latest progress on graph representation learning and their applications in different fields. We hope more advanced technologies can be proposed or inspired, and also we expect that the direction of graph representation learning can catch much more attention in both academic and industry.
引用
收藏
页码：2997 / 2998
页数：2
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