Maximally modular structure of growing hyperbolic networks
arXiv:2206.08773 · doi:10.1038/s42005-023-01182-4
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.
References in corpus (23)
- Emergence of scaling in random networks
- Fast unfolding of communities in large networks
- Statistical mechanics of complex networks
- Finding and evaluating community structure in networks
- Community detection in graphs
- Finding community structure in very large networks
- Resolution limit in community detection
- Temporal Networks
- Community detection in networks: A user guide
- Growing Scale-Free Networks with Tunable Clustering
- Hyperbolic Geometry of Complex Networks
- Modularity from Fluctuations in Random Graphs and Complex Networks
- Popularity versus Similarity in Growing Networks
- Self-similarity of complex networks and hidden metric spaces
- Triadic closure as a basic generating mechanism of communities in complex networks
- A Model for Social Networks
- On community structure in complex networks: challenges and opportunities
- Network Mapping by Replaying Hyperbolic Growth
- Emergence of Soft Communities from Geometric Preferential Attachment
- A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities
- Soft communities in similarity space
- The inherent community structure of hyperbolic networks
- Growing hyperbolic networks beyond two dimensions: the generalised popularity-similarity optimisation model