Topological Data Analysis on Networks – Applications and Scalability issues

Authors

Cuneyt Gurcan Akcora, University of Manitoba

Document Type

Presentation Abstract

Presentation Date

9-21-2020

Abstract

Over the last couple of years, Topological Data Analysis (TDA) has seen a growing interest from Data Scientists of diverse backgrounds. TDA is an emerging field at the interface of algebraic topology, statistics, and computer science. The key rationale in TDA is that the observed data are sampled from some metric space and the underlying unknown geometric structure of this space is lost because of sampling. TDA recovers the lost underlying topology.

We aim at adapting TDA algorithms to work on networks and overcoming the scalability issues that arise while working on large networks. In this talk, I will outline our three alternative approaches in applying Persistent Homology and TDAMapper based Topological Data Analysis algorithms to Blockchain networks.

Additional Details

September 21, 2020 at 3:00 p.m. via Zoom

Recommended Citation

Gurcan Akcora, Cuneyt, "Topological Data Analysis on Networks – Applications and Scalability issues" (2020). Colloquia of the Department of Mathematical Sciences. 600.
https://scholarworks.umt.edu/mathcolloquia/600