Scalable Vector Representation for Topological Data Analysis Based Classification

Tananun Songdechakraiwut, Bryan M Krause, Matthew I Banks, Kirill V Nourski, Barry D Van Veen

Keywords: Topological data analysis, classification, Wasserstein distance, networks, graphs

TL;DR: This paper presents a computationally tractable approach to topological classification of networks by using persistent homology and optimal transport to define a novel Wasserstein distance-preserving vector representation for topological features.

Abstract: Classification of large and dense networks based on topology is very difficult due to the computational challenges of extracting meaningful topological features from real-world networks. In this paper we present a computationally tractable approach to topological classification of networks by using principled theory from persistent homology and optimal transport to define a novel vector representation for topological features. The proposed vector space is based on the Wasserstein distance between persistence barcodes. The 1-skeleton of the network graph is employed to obtain 1-dimensional persistence barcodes that represent connected components and cycles. These barcodes and the corresponding Wasserstein distance can be computed very efficiently. The effectiveness of the proposed vector space is demonstrated using support vector machines to classify brain networks. This extended abstract is adapted from the extended work available at https://arxiv.org/abs/2202.01275.