Matrix growth models based on centrality measures: a first analysis

Abstract

A general growth model of random networks based on centrality measures is introduced. This formalism extends the well-known models of prefer- ential attachment. We propose to set the preferential attachment using a linear function of some centrality measures ranging from local to global scale. The aim is to include spectral measures, such as PageRank and Bonacich, and geodesic measures, such as betweenness and closeness. In this paper we present a first analysis using degree and Personalized PageRank.