Abstract

Graph theory models entities and their relationships using nodes connected by edges, serving as an essential tool in various domains. With the rich information that graphs encapsulate, there is a growing interest in exploring the underlying complex structures and phenomena they contain. This exploration is vital for advancing knowledge and developing innovative solutions in these fields. Thus, this talk presents the usage of machine learning techniques for solving graph optimization problems, highlighting the challenges of graph representation learning. Our proposed model uniquely captures both local and global structural information and accurately represents the information of intermediate solutions. In this talk, we also address the question of knowledge transferability across different graph optimization problems. Our proposed framework demonstrates the ability of models to generalize and rapidly adapt across various graph optimization problems.

Bio.

Hui-Ju Hung is an assistant professor in the Department of Computer Science and Information Engineering at National Central University, Taiwan. She received her Ph.D. in the Department of Computer Science and Engineering at the Pennsylvania State University in 2024. Her research focuses on algorithm design for graph optimization problems in social networks and machine learning approaches to solving graph optimization problems. Her work has been published in WWW, ACM KDD, ACM CIKM, IEEE ICDCS, and IEEE GLOBECOM.