Hierarchical team structure and multidimensional localization (or siloing) on networks

Abstract

Abstract Knowledge silos emerge when structural properties of organizational interaction networks limit the diffusion of information. These structural barriers are known to take many forms at different scales—hubs in otherwise sparse organizations, large dense teams, or global core-periphery structure—but we lack an understanding of how these different structures interact and shape dynamics. Here we take a first theoretical step in bridging the gap between the mathematical literature on localization of spreading dynamics and the more applied literature on knowledge silos in organizational interaction networks. To do so, we introduce a new model that considers a layered structure of teams to unveil a new form of hierarchical localization (i.e. the localization of information at the top or center of an organization) and study its interplay with known phenomena of mesoscopic localization (i.e. the localization of information in large groups), k-core localization (i.e. around denser subgraphs) and hub localization (i.e. around high degree stars). We also include a complex contagion mechanism by considering a general infection kernel which can depend on hierarchical level (influence), degree (popularity), infectious neighbors (social reinforcement) or team size (importance). This very general model allows us to explore the multifaceted phenomenon of information siloing in complex organizational interaction networks and opens the door to new optimization problems to promote or hinder the emergence of different localization regimes.