# Discussion on "Sparse graphs using exchangeable random measures" by F. Caron and E. B. Fox

## TL;DR

This paper discusses the GGP model for sparse graphs, comparing it with ER and preferential attachment models using various network measures to analyze structural differences.

## Contribution

It provides an analysis of the GGP model relative to ER and preferential attachment models using multiple network metrics.

## Key findings

- GGP model exhibits distinct structural properties compared to ER and AB models.
- Analysis highlights differences in connected components, clustering, and core node share.
- Provides insights into the applicability of GGP for modeling real-world sparse graphs.

## Abstract

Discussion on "Sparse graphs using exchangeable random measures" by F. Caron and E. B. Fox. In this discussion we contribute to the analysis of the GGP model as compared to the Erdos-Renyi (ER) and the preferential attachment (AB) models, using different measures such as number of connected components, global clustering coefficient, assortativity coefficient and share of nodes in the core.