Centrality scaling in large networks

TL;DR

This paper introduces a multiscale decomposition method for betweenness centrality in large networks, enabling efficient prediction of centrality distributions and practical analysis of networks with millions of nodes.

Contribution

A novel multiscale approach that predicts betweenness centrality distributions in large networks, reducing computational complexity.

Findings

The method accurately predicts betweenness distributions in large networks.

Application to a social network with over 5 million nodes demonstrates scalability.

Betweenness contributions follow a characteristic scaling law.

Abstract

Betweenness centrality lies at the core of both transport and structural vulnerability properties of complex networks, however, it is computationally costly, and its measurement for networks with millions of nodes is near impossible. By introducing a multiscale decomposition of shortest paths, we show that the contributions to betweenness coming from geodesics not longer than L obey a characteristic scaling vs L, which can be used to predict the distribution of the full centralities. The method is also illustrated on a real-world social network of 5.5*10^6 nodes and 2.7*10^7 links.