Exponential random graph modeling of communication networks to understand organizational crisis

Abstract

In recent social network studies, exponential random graph models have been used comprehensively to model global social network structure as a function of their local features. In this study, we describe the exponential random graph models and demonstrate its use in modeling the changing communication network structure at Enron Corporation during the period of its disintegration. We illustrate the modeling on communication networks and provide a new way of classifying networks and their performance based on the occurrence of their local features. Among several micro-level structures of exponential random graph models, we found significant variation in the appearance of A2P (Alternating k-two-paths) network structure in the communication network during crisis period and non-crisis period. This finding could also be used in analyzing communication networks of dynamic project groups and their adaptation process during crisis which could lead to an improved understanding how communications network evolve and adapt during crisis. © 2011 ACM.