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Emergence of soft communities from geometric preferential attachment
Konstantin Zuev1, Marián Boguñá2, Ginestra Bianconi3
1Department of Physics, Northeastern University, Boston MA 02115, USA.
Scientific Reports
|April 30, 2015
Summary
We introduce geometric preferential attachment (GPA), a new mechanism explaining common network properties like scale-free distributions and community structure in growing networks. This model offers insights into internet structure and its relation to cosmological models.
Area of Science:
- Network science
- Complex systems
- Statistical physics
Background:
- Real-world networks exhibit common structural properties, including scale-free degree distributions, strong clustering, and community structure.
- Existing generative mechanisms struggle to explain these three properties simultaneously in growing networks.
Purpose of the Study:
- To propose a novel generative mechanism that explains multiple common structural properties in growing networks.
- To introduce and validate the geometric preferential attachment (GPA) model.
Main Methods:
- Developed the geometric preferential attachment (GPA) model, incorporating latent network geometry and preferential attachment.
- Validated the GPA model against empirical data from the Internet.
Main Results:
- The GPA mechanism successfully generates networks with scale-free degree distributions, strong clustering, and community structure.
- GPA produces 'soft communities,' offering a new perspective on network community structure.
- The model demonstrates connections to cosmological concepts like inflation.
Conclusions:
- Geometric preferential attachment (GPA) provides a unified explanation for key network properties in growing systems.
- The GPA model offers a novel framework for understanding network formation and structure.
- Potential links between network science and cosmology are highlighted.
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