Complexity in human transportation networks: A comparative analysis of worldwide air transportation and global cargo ship movements

TL;DR

This paper compares the global air and cargo ship transportation networks, revealing statistical similarities, introducing a new analysis technique, and discussing implications for network resilience and universal dynamics.

Contribution

It introduces a novel technique based on effective distances and shortest paths for analyzing weighted networks, revealing insights into network resilience and universal dynamic behaviors.

Findings

Both networks show similar statistical properties despite topological differences.

Effective shortest-path distances correlate strongly with node centrality.

Network resilience can be analyzed more precisely using the new technique.

Abstract

We present a comparative network theoretic analysis of the two largest global transportation networks: The worldwide air-transportation network (WAN) and the global cargoship network (GCSN). We show that both networks exhibit striking statistical similarities despite significant differences in topology and connectivity. Both networks exhibit a discontinuity in node and link betweenness distributions which implies that these networks naturally segragate in two different classes of nodes and links. We introduce a technique based on effective distances, shortest paths and shortest-path trees for strongly weighted symmetric networks and show that in a shortest-path-tree representation the most significant features of both networks can be readily seen. We show that effective shortest-path distance, unlike conventional geographic distance measures, strongly correlates with node centrality measures. Using the new technique we show that network resilience can be investigated more precisely than with contemporary techniques that are based on percolation theory. We extract a functional relationship between node characteristics and resilience to network disruption. Finally we discuss the results, their implications and conclude that dynamic processes that evolve on both networks are expected to share universal dynamic characteristics.