Stations, trains and small-world networks

TL;DR

This paper analyzes urban train networks' small-world properties by calculating clustering, path length, and degree, comparing results with theoretical models and examining how architecture influences these properties.

Contribution

It provides a comparative analysis of real train networks against theoretical bipartite graph models to understand architectural effects on small-world characteristics.

Findings

Urban train networks exhibit small-world properties.

Architectural differences influence network small-world metrics.

Comparison with bipartite graph models reveals deviations and similarities.

Abstract

The clustering coefficient, path length and average vertex degree of two urban train line networks have been calculated. The results are compared with theoretical predictions for appropriate random bipartite graphs. They have also been compared with one another to investigate the effect of architecture on the small-world properties.