Topological Classification in a Wasserstein Distance Based Vector Space

TL;DR

This paper introduces a new vector space representation for classifying large networks based on topology, utilizing persistent homology and Wasserstein distances for efficient computation and improved classification accuracy.

Contribution

It presents a novel topological vector space based on Wasserstein distance between persistence barcodes for network classification.

Findings

Effective classification of simulated networks achieved.

Functional brain networks classified successfully.

Wasserstein-based vector space is computationally efficient.

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 simulated networks and measured functional brain networks.