The University of Arizona
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Topological and geometric methods for graph analysis


Topological and geometric methods for graph analysis
Series: TRIPODS Seminar
Location: ENR2 S210
Presenter: Yusu Wang, Professor of Computer Science and Engineering and Faculty co-Lead for the Foundations CoP at Translational Data Analytics Institute, Ohio State University
In recent years, topological and geometric data analysis (TGDA) has emerged as a new and promising field for processing, analyzing and understanding complex data. Indeed, geometry and topology form natural platforms for data analysis, with geometry describing the ``shape'' and ``structure'' behind data; and topology characterizing / summarizing both the domain where data are sampled from, as well as functions and maps associated to them.

In this talk, I will show how topological and geometric ideas can be used to analyze graph data, which occurs ubiquitously across science and engineering. Graphs could be geometric in nature, such as road networks in GIS, or relational and abstract, such as protein-protein interaction networks. I will particularly focus on the reconstruction of hidden geometric graphs from noisy data, as well as graph matching and classification. I will discuss the motivating applications, algorithm development, and theoretical guarantees for these methods. Through these topics, I aim to illustrate the important role that geometric and topological ideas can play in data analysis.