2014/08/13 by Manlio De Domenico, Andrea Lancichinetti, Alex Arenas +1 · 207 citations
Computer Science · Decision Sciences · Physics and Astronomy · #Auger #Complex Network Analysis Techniques #Complex network #Complex system #Flow (mathematics) #Information flow #Modular design #Modularity (biology) #Opinion Dynamics and Social Influence #Overhead (engineering) #Scientific Computing and Data Management #cs.SI #physics.soc-ph
paper · pdf · doi:10.1103/physrevx.5.011027
published in Physical Review X 5(1) (American Physical Society)
arxiv created 2014/08/13 · openalex publication_date 2015/03/06 · arxiv updated 2015/04/24 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
To comprehend interconnected systems across the social and natural sciences, researchers have developed many powerful methods to identify functional modules. For example, with interaction data aggregated into a single network layer, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow processes. However, many interconnected systems consist of agents or components that exhibit multiple layers of interactions, possibly from several different processes. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here, we propose a method based on a compression of network flows that can identify modular flows both within and across layers in nonaggregated multilayer networks. Our numerical experiments on synthetic multilayer networks, with some layers originating from the same interaction process, show that the analysis fails in aggregated networks or when treating the layers separately, whereas the multilayer method can accurately identify modules across layers that originate from the same interaction process. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks with topics as layers: scientists affiliated with the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method uncovers connected topics and reveals smaller modules with more overlap that better capture the actual organization.