LOGS第2023/08/26期|| 新加坡管理大学方元：​ Low-Resource Learning on Graphs

独行速，众行远。图学习研讨会（LOGS）公众号不定期地举行图学习以及机器学习相关的研讨会，邀请相关领域的专家，一线科研人员和顶会论文作者进行分享，希望能够给大家提供一个相互交流，研讨，和学习的平台。如果您有相关的研究， 想要研讨与分享，或者有感兴趣的topic和论文也欢迎给我们留言。本期的嘉宾是来自新加坡管理大学方元，他将会为我们带来面向低资源的图学习方法的精彩内容。

报告内容

报告摘要

Graph structures are ubiquitous in various domains, ranging from social networks and e-commerce platforms to transportation and biological systems. On these graphs, various graph-based analytics and mining tasks exist, many of which can be cast as instances of link prediction, node classification, and graph classification. Moving away from manual feature engineering, graph neural networks (GNN) have witnessed widespread success in various application scenarios due to their ability to learn powerful graph representations automatically. However, their success is often dependent on the availability and quality of graph structures and labeled data, without which their performance can suffer. In this talk, we explore alternative learning paradigms different from the traditional supervised learning paradigm, specifically addressing two types of low-resource scenarios on graphs: structure scarcity and label scarcity. We will first provide an overview of low-resource problems and methods on graphs, and then introduce some of our representative works on these problems.

分享嘉宾

Since July 2018, Dr Yuan Fang has been an Assistant Professor at the School of Computing and Information Systems, Singapore Management University (SMU). Prior to joining SMU, he was a data scientist at DBS Bank, and a research scientist at A*STAR. He obtained a PhD Degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014, and Bachelor of Computing with First Class Honors from National University of Singapore in 2009. His general interest lies in the broad areas of data mining, machine learning and artificial intelligence. More specifically, he is working on graph-based learning and recommendation systems.