Statistical Network Topology for Crisis Informetrics

TL;DR

This paper explores the scale-free properties of organizational communication networks during crises, analyzing static and dynamic topologies and confirming the prevalence of power law distributions in email networks.

Contribution

It introduces an analysis of both static and dynamic social network topologies during crises, highlighting the scale-free nature of email communication networks.

Findings

Both static and dynamic networks show scale-free characteristics.

Email communication networks exhibit high degree of scale-free behavior.

Actor prominence remains stable across different network aggregations.

Abstract

Crisis informetrics is considered to be a relatively new and emerging area of research, which deals with the application of analytical approaches of network and information science combined with experimental learning approaches of statistical mechanics to explore communication and information flow, robustness as well as tolerance of complex crisis networks under threats. In this paper, we discuss the scale free network property of an organizational communication network and test both traditional (static) and dynamic topology of social networks during organizational crises Both types of topologies exhibit similar characteristics of prominent actors reinforcing the power law distribution nature of scale free networks. There are no significant fluctuations among the actor prominence in daily and aggregated networks. We found that email communication network display a high degree of scale free behavior described by power law.