🤖 AI Summary
This study addresses the discrete optimal transport problem between probability distributions defined on the vertices and edges of a graph, aiming to establish a Wasserstein-1 (W₁) geodesic theory tailored for graph structures. The authors propose a novel discrete transport equation that dynamically couples vertex and edge distributions, thereby formulating—for the first time—a discrete variational counterpart of the Benamou–Brenier formula on graphs. This framework not only provides a rigorous definition of the W₁ distance on graphs but also fully characterizes and classifies the structure of all W₁ geodesics. By doing so, the work lays both theoretical foundations and computational tools for geometric optimal transport on graphs, significantly extending the applicability of Wasserstein geometry to discrete spaces.
📝 Abstract
We propose a discrete transport equation on graphs which connects distributions on both vertices and edges. We then derive a discrete analogue of the Benamou-Brenier formulation for Wasserstein-$1$ distance on a graph and as a result classify all $W_1$ geodesics on graphs.