SOURCE-LINKED INTELLIGENCE

Optimal Transport for Network Comparison: A Unified Review with New Spectral Bounds and Machine Learning Applications

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

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 simple 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 Wa

Read original source ↗ Open in workspace

recordType

paper

region

Global