Towards a general method to classify personal network structures

Abstract

In this study, we present a method to uncover the fundamental dimensions of structural variability in Personal Networks (PNs) and develop a classification solely based on these structural properties. We address the limitations of previous literature and lay the foundation for a rigorous methodology to construct a Structural Typology of PNs. We test our method with a dataset of nearly 8,000 PNs belonging to high school students. We find that the structural variability of these PNs can be described in terms of six basic dimensions encompassing community and cohesive subgroup structure, as well as levels of cohesion, hierarchy, and centralization. Our method allows us to categorize these PNs into eight types and to interpret them structurally. We assess the robustness and generality of our methodology by comparing with previous results on structural typologies. To encourage its adoption, its improvement by others, and to support future research, we provide a publicly available Python class, enabling researchers to utilize our method and test the universality of our results.