Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks

In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework. We show that the addition of management layers can significantly improve spreading processes on both random geometric graphs and empirical corporate networks. Additionally, we show that utilizing a more centralized working relationship network rather than a strict administrative network further increases overall innovation reach. In fact, this more centralized structure in conjunction with management layers is essential to both reaching a plurality of nodes and creating a stable adopted community in the long time horizon. Further, we show that the selection of seed nodes affects the final stability of the adopted community, and while the most influential nodes often produce the highest peak adoption, this is not always the case. In some circumstances, seeding nodes near but not in the highest positions in the graph produces larger peak adoption and more stable long-time adoption.

Automated summary

In this paper, the effects of corporate hierarchies on innovation spread across multilayer networks are explored using an elaborated SIR framework. It is found that adding management layers can significantly improve spreading processes, and utilizing a more centralized working relationship network can further increase innovation reach. The selection of seed nodes also affects the stability of the adopted community, with nodes near high positions sometimes producing larger and more stable peak adoption.