# Random-Graph Models and Characterization of Granular Networks

## TL;DR

This paper evaluates various network measures to characterize and distinguish between different spatial random-graph models and empirical granular networks, revealing key differences across dimensions and model plausibility.

## Contribution

It identifies network measures capable of differentiating physically plausible models from unphysical ones and highlights dimensional differences in network measure distributions.

## Key findings

- Certain network measures distinguish plausible from unphysical models.
- Significant differences in measure distributions between 2D and 3D networks.
- Implications for understanding experimental granular networks.

## Abstract

Various approaches and measures from network analysis have been applied to granular and particulate networks to gain insights into their structural, transport, failure-propagation and other systems-level properties. In this article, we examine a variety of common network measures and study their ability to characterize various two-dimensional and three-dimensional spatial random-graph models and empirical two-dimensional granular networks. We identify network measures that are able to distinguish between physically plausible and unphysical spatial network models. Our results also suggest that there are significant differences in the distributions of certain network measures in two and three dimensions, hinting at important differences that we also expect to arise in experimental granular networks.