Recent Advances in Machine Learning on Graphs
Graphs are data structures that express the connection relationships between individuals and are widely used to express various phenomena in real life. Representative examples include user’s social networks, knowledge graphs, molecular structure graphs, protein reaction graphs, and gene graphs. In order to improve the performance of graph-based machine learning models, it is essential to learn the representation of nodes and edges in consideration of the structure of the graph. To this end, with the recent development of deep learning technology, machine learning techniques for graph analysis are in the spotlight. This seminar introduces the latest technology and research trends based on deep learning for graph representation learning, and introduces some interesting applications of graph machine learning technology.
Chanyoung Park is currently an assistant professor in the department of industrial and systems engineering at KAIST. Before joining KAIST, he worked as a postdoctoral research fellow at University of Illinois at Urbana-Champaign working with Prof. Jiawei Han. He received the Ph.D degree in Computer Science and Engineering from POSTECH in 2019. His research focuses on mining meaningful knowledge from multimodal data to develop artificial intelligence solutions for various real-world applications across different disciplines.
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