Use of a hierarchical deconstruction procedure for the classification of personal networks: Exploring nested groups around you

Abstract

The deconstruction of personal networks through the iterative elimination of the nodes with the highest betweenness centrality is an effective strategy for describing their structural properties. In this study we show that iterative deconstruction can also serve to classify personal networks into typologies. With longitudinal data from a sample of 69 students, we found that denser networks are more resistant to fragmentation while those organized in defined subgroups usually have a shorter deconstruction process. In addition, the deconstruction strategy allows knowing the hierarchical structure of personal networks, made up of nested subgroups.