Categorical Framework for Complex Organizational Networks: Understanding the Effects of Types, Size, Layers, Dynamics and Dimensions

Abstract

Organizational network modeling can exhibit complexity in many forms to embrace the reality of an organization’s processes and capabilities. Networks enable modelers to account for many structural and attributional elements of organizations in ways that can be more powerful than statistical data mining methods or stochastic models. However, the price paid for this increased modeling strength can come in the form of increased complexity, sensitivity and fragility. Traditional network methods and measures can be sensitive to changing, unknown, or inaccurate topology; fragile to dynamic and algorithmic processing; and computationally stressed when incorporating high-dimensional data. Sensitivity and fragility of network models can be managed by setting boundaries around network states, within which specific geometries and topologies can be robustly measured. We propose a categorical framework that identifies such boundaries and develops appropriate modeling methodology and measuring tools for various categories of organizational networks. Categorization of networks along important dimensions such as type, size, layers, dynamics and dimensions provide boundaries of paradigm shifts (from a social scientific perspective) or phase transitions (from physical sciences) – points at which the fundamental properties or dynamics of the networks change. Not adjusting for these categorical issues can lead to poor methodology, flawed analysis, and deficient results. The purpose of our work is to: 1) develop a framework to enable the construction of a network organizational modeling theory, and 2) identify measures, methods and tools that are appropriate for specific categories (and inappropriate for others) within this field of study. We believe that such a framework can help guide underlying theory and serve as a basis for further formalization of network studies.