Maximally modular structure of growing hyperbolic networks

Abstract

Hyperbolic models are remarkably good at reproducing the scale-free, highly clustered and small-world properties of networks representing real complex systems in a very simple framework. Here we show that for the popularity-similarity optimization model from this family, the generated networks become also extremely modular in the thermodynamic limit, in spite of lacking any explicit community formation mechanism in the model definition. According to our analytical results supported by numerical simulations, when the system size is increased, the modularity approaches one surprisingly fast.

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Cited by in corpus (4)

• Greedy routing optimisation in hyperbolic networks
• Community detection in hypergraphs through hyperedge percolation
• Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularity
• Symmetry-driven embedding of networks in hyperbolic space