Revealing the Micro-Structure of the Giant Component in Random Graph Ensembles

TL;DR

This paper provides exact analytical insights into the micro-structure of the giant component in random graphs, revealing the importance of degree correlations for network connectivity and properties.

Contribution

It introduces exact formulas for degree distributions and correlations within the giant component of configuration model networks, highlighting their role in network properties.

Findings

Degree distributions on the giant component differ from the overall network.

Degree-degree correlations are crucial for the giant component's integrity.

Using giant component degree distributions improves predictions of network properties.

Abstract

The micro-structure of the giant component of the Erd{\H o}s-R\'enyi network and other configuration model networks is analyzed using generating function methods. While configuration model networks are uncorrelated, the giant component exhibits a degree distribution which is different from the overall degree distribution of the network and includes degree-degree correlations of all orders. We present exact analytical results for the degree distributions as well as higher order degree-degree correlations on the giant components of configuration model networks. We show that the degree-degree correlations are essential for the integrity of the giant component, in the sense that the degree distribution alone cannot guarantee that it will consist of a single connected component. To demonstrate the importance and broad applicability of these results, we apply them to the study of the distribution of shortest path lengths on the giant component, percolation on the giant component and the spectra of sparse matrices defined on the giant component. We show that by using the degree distribution on the giant component, one obtains high quality results for these properties, which can be further improved by taking the degree-degree correlations into account. This suggests that many existing methods, currently used for the analysis of the whole network, can be adapted in a straightforward fashion to yield results conditioned on the giant component.