Optimal Transport for Network Comparison: A Review with Machine Learning Applications

Abstract

Network comparison using optimal transport is a growing area of research in network science. Unlike standard graph metrics, optimal transport computes both network dissimilarity and a transport plan that explains how one graph morphs into another. In this paper, we review how optimal transport compares undirected, unweighted graphs using three primary distances: the Wasserstein, Gromov-Wasserstein, and Bures-Wasserstein distances. We examine the closed form of the Wasserstein distance in one dimension via node feature probability distributions, and show how the transport plans of the Wasserstein and Gromov-Wasserstein distances capture which specific nodes influence the distance after graph perturbation. For the Bures-Wasserstein distance, we derive bounds using Laplacian spectra to bypass full spectral decompositions. Finally, we evaluate these distances using a synthetic network dataset for clustering and a real-world time series network for anomaly detection.