Gromov-Wasserstein Learning: A New Machine Learning Framework for Structured Data Analysis

Seminar Series

Monday, March 2, 2020 - 12:00
LSRC D106

Abstract: Many biomedical data types like protein-protein interaction (PPI) networks and biological molecules are structured data, which are represented as graphs optionally accompanied with node attributes. From the viewpoint of machine learning, tasks focusing on these structured data, such as network alignment and molecule analysis, can often be formulated as graph representation, matching, partitioning, and clustering problems. Unfortunately, due to their NP-completeness, we have to rely on heuristic methods to solve these problems in practice, often without theoretical support for stability and rationality. 
 
In this talk, I will introduce a novel machine learning framework called Gromov-Wasserstein Learning (GWL) — a new systematic solution I proposed for structured data analysis. First, I will introduce the theoretical fundamentals of GWL and link it to learning tasks from structured data. Next, I will describe the optimization algorithms in the GWL, analyzing their convergence, computational complexity, and scalability in detail. Finally, I will show that the GWL unifies graph matching, partitioning, and representation into the same algorithmic framework, which outperforms existing methods on PPI network analysis and molecule clustering and classification. 

Speaker: Hongteng Xu, PhD
Senior Research Scientist
Infinia ML Inc.

Visiting Researcher
Department of Electrical and Computer Engineering
Pratt School of Engineering
Duke University