Identifying modular flows on multilayer networks reveals highly overlapping organization in social systems

Abstract

Unveiling the community structure of networks is a powerful methodology to comprehend interconnected systems across the social and natural sciences. To identify different types of functional modules in interaction data aggregated in a single network layer, researchers have developed many powerful methods. For example, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow in the systems they represent. However, many networked systems consist of agents or components that exhibit multiple layers of interactions. 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 compression of network flows that can identify modular flows in non-aggregated multilayer networks. Our numerical experiments on synthetic networks show that the method can accurately identify modules that cannot be identified in aggregated networks or by analyzing the layers separately. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks: scientists affiliated to the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method reveals smaller modules with more overlap that better capture the actual organization.

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• Mapping multiplex hubs in human functional brain network
• Modeling sequences and temporal networks with dynamic community structures
• Detectability thresholds and optimal algorithms for community structure in dynamic networks
• Clustering Network Layers With the Strata Multilayer Stochastic Block Model
• Mapping higher-order network flows in memory and multilayer networks with Infomap
• Benchmark model to assess community structure in evolving networks
• Emergence of multiplex communities in collaboration networks
• Visibility graphs for image processing
• Multilink Communities of Multiplex Networks
• Geometric correlations mitigate the extreme vulnerability of multiplex networks against targeted attacks
• Mesoscopic Structures Reveal the Network Between the Layers of Multiplex Datasets
• Characterizing the analogy between hyperbolic embedding and community structure of complex networks
• Constrained information flows in temporal networks reveal intermittent communities
• A Framework for the Construction of Generative Models for Mesoscale Structure in Multilayer Networks
• Percolation in real multiplex networks
• The topological Dirac equation of networks and simplicial complexes
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• Disentangling homophily, community structure and triadic closure in networks
• Enhancing transport properties in interconnected systems without altering their structure
• Different approaches to community detection
• Map Equation Centrality: Community-aware Centrality based on the Map Equation
• Dynamical origins of the community structure of multi-layer societies
• The Minimum Description Length Principle for Pattern Mining: A Survey
• Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization
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• The International Postal Network and Other Global Flows As Proxies for National Wellbeing
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