Flow Distances on Open Flow Networks

TL;DR

This paper introduces flow distances, a new way to measure node-to-node distances in open flow networks, enabling visualization, centrality analysis, and clustering in systems like ecological webs and economic networks.

Contribution

The paper presents a novel theoretical framework for flow distances in open flow networks, explicitly expressed through Markov matrices, and demonstrates their application in real-world networks.

Findings

Flow distances differ from traditional random walk distances due to network openness.

Flow distances can visualize network structures and identify key nodes.

Applications include ranking and clustering sectors in economic networks and analyzing species in food webs.

Abstract

Open flow network is a weighted directed graph with a source and a sink, depicting flux distributions on networks in the steady state of an open flow system. Energetic food webs, economic input-output networks, and international trade networks, are open flow network models of energy flows between species, money or value flows between industrial sectors, and goods flows between countries, respectively. Flow distances (first-passage or total) between any given two nodes $i$ and $j$ are defined as the average number of transition steps of a random walker along the network from $i$ to $j$ under some conditions. They apparently deviate from the conventional random walk distance on a closed directed graph because they consider the openness of the flow network. Flow distances are explicitly expressed by underlying Markov matrix of a flow system in this paper. With this novel theoretical conception, we can visualize open flow networks, calculating centrality of each node, and clustering nodes into groups. We apply flow distances to two kinds of empirical open flow networks, including energetic food webs and economic input-output network. In energetic food webs example, we visualize the trophic level of each species and compare flow distances with other distance metrics on graph. In input-output network, we rank sectors according to their average distances away other sectors, and cluster sectors into different groups. Some other potential applications and mathematical properties are also discussed. To summarize, flow distance is a useful and powerful tool to study open flow systems.