Detecting Communities when Order and Direction Matter in Social Network Analysis

A novel algorithm combining both node degree and edge betweenness (node degree+edge betweenness) is developed to highlight gradients in connectivity. It identifies communities within complex directed graphs. These are networks that are composed of actors who have ties that originate and end at themselves (self-loops). Hierarchies in social partitioning for node degree (in-ties, out-ties) are evident among actors when applying the node degree+edge betweenness algorithm to network datasets. This observed gradient characteristic has useful application in epidemiological and health services research where the direction and temporality of ties matter in activities such as contact tracing and the deployment of resources. Our new algorithm is readily available in the R package ig.degree.betweenness and in the Python module ig-degree-betweenness.