Networks with the Smallest Average Distance and the Largest Average Clustering

TL;DR

This paper characterizes graph structures that optimize for minimal average distance and maximal clustering, analyzing their properties, sensitivity to rewiring, and robustness to vertex removal.

Contribution

It identifies unique and multiple graph configurations with optimal clustering and distance, providing analytical insights and robustness enhancement methods.

Findings

Typically unique graphs with maximal clustering and minimal average distance.

Multiple graphs with minimal average distance but varying clustering.

Rewiring affects clustering coefficient and robustness can be improved.

Abstract

We describe the structure of the graphs with the smallest average distance and the largest average clustering given their order and size. There is usually a unique graph with the largest average clustering, which at the same time has the smallest possible average distance. In contrast, there are many graphs with the same minimum average distance, ignoring their average clustering. The form of these graphs is shown with analytical arguments. Finally, we measure the sensitivity to rewiring of this architecture with respect to the clustering coefficient, and we devise a method to make these networks more robust with respect to vertex removal.