Stability of Local Information based Centrality Measurements under Degree Preserving Randomizations

TL;DR

This paper investigates the stability of six local information-based centrality measures under degree-preserving randomizations, analyzing their robustness across different network types and assortativity levels.

Contribution

It introduces a novel approach to assess the stability of local centrality metrics under degree-preserving randomizations, with implications for various network analysis applications.

Findings

Stability varies with assortativity levels.

Local centrality measures show robustness under certain conditions.

Analysis applies to both scale-free and exponential networks.

Abstract

Node centrality is one of the integral measures in network analysis with wide range of applications from socio-economic to personalized recommendation. We argue that an effective centrality measure should undertake stability even under information loss or noise introduced in the network. With six local information based centrality metric, we investigate the effect of varying assortativity while keeping degree distribution unchanged, using networks with scale free and exponential degree distribution. This model provides a novel scope to analyze stability of centrality metric which can further finds many applications in social science, biology, information science, community detection and so on.