Towards a General Typology of Personal Network Structures
In this study, we aim 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 develop a robust, unbiased Structural Typology of PNs. We find a way to effectively describe the structural variability of PNs in terms of six basic dimensions encompassing community and cohesive subgroup structure, as well as levels of cohesion, hierarchy, and centralization. Furthermore, we categorize PNs into eight types, and interpret them structurally. We assess the robustness and generality of our typology. To encourage its adoption and support future research, we provide a publicly available Python class, enabling researchers to utilize our methodology and test the universality of the proposed typology.
Clustering, Dimensionality Reduction, Personal Networks, Social Networks Analysis, Structural Typology