Generalized Network Growth: from Microscopic Strategies to the Real Internet Properties

TL;DR

This paper introduces a generalized network growth model that links microscopic agent strategies to large-scale Internet properties, accurately reproducing key network features at the Autonomous System level.

Contribution

The paper presents a novel generalized model for network growth that incorporates both new vertices and edges, capturing the disassortative mixing observed in the Internet.

Findings

Model accurately reproduces degree distribution

Model matches betweenness distribution

Model replicates clustering coefficient and degree correlations

Abstract

In this paper we present a generalized model for network growth that links the microscopical agent strategies with the large scale behavior. This model is intended to reproduce the largest number of features of the Internet network at the Autonomous System (AS) level. Our model of network grows by adding both new vertices and new edges between old vertices. In the latter case a ``rewarding attachment'' takes place mimicking the disassortative mixing between small routers to larger ones. We find a good agreement between experimental data and the model for the degree distribution, the betweenness distribution, the clustering coefficient and the correlation functions for the degrees.