Cohesion and segregation in higher-order networks

Abstract: Looking to overcome the limitations of traditional networks, the network science community has lately given much attention to the so-called higher-order networks, where group interactions are modeled alongside pairwise ones. While degree distribution and clustering are the most important features of traditional network structure, higher-order networks present two additional fundamental properties that are barely addressed: the group size distribution and overlaps. Here, I investigate the impact of these properties on the network structure, focusing on cohesion and segregation (fragmentation and community formation). For that, I create artificial higher-order networks with a version of the configuration model that assigns degree to nodes and size to groups and forms overlaps with a tuning parameter $p$. Counter-intuitively, the results show that a high frequency of overlaps favors both network cohesion and segregation -- the network becomes more modular and can even break into several components, but with tightly-knit communities.