Border Detection in Complex Networks

TL;DR

This paper introduces a formal method to identify border and internal nodes in complex networks using node diversity, providing new insights into network structure through theoretical and real-world examples.

Contribution

It presents a novel, objective definition of network borders based on node diversity and a methodology for their identification, applicable to various network types.

Findings

Node diversity is uncorrelated with traditional network metrics.

The border detection method works on theoretical and real-world networks.

Insights into network structure and organization were obtained.

Abstract

One important issue implied by the finite nature of real-world networks regards the identification of their more external (border) and internal nodes. The present work proposes a formal and objective definition of these properties, founded on the recently introduced concept of node diversity. It is shown that this feature does not exhibit any relevant correlation with several well-established complex networks measurements. A methodology for the identification of the borders of complex networks is described and illustrated with respect to theoretical (geographical and knitted networks) as well as real-world networks (urban and word association networks), yielding interesting results and insights in both cases.