Generating random networks with given degree-degree correlations and degree-dependent clustering

Area of Science:

• Network Science
• Complex Systems Modeling
• Statistical Physics

Background:

• Random networks are fundamental tools for modeling complex systems in diverse scientific fields.
• Accurately approximating real-world networks requires precise control over their statistical properties.
• Existing models often lack the flexibility to tune specific network characteristics like degree correlations and clustering.

Purpose of the Study:

• To present a new algorithm for generating random networks with arbitrary degree-degree correlations.
• To enable adjustable degree-dependent clustering within these generated networks.
• To provide a method for refining network properties based on empirical data.

Main Methods:

• Development of a novel network construction algorithm.
• Incorporation of adjustable parameters for degree-degree correlations.
• Implementation of a mechanism for controlling degree-dependent clustering.
• Validation using real-world empirical network data.

Main Results:

• The algorithm successfully constructs random networks with user-defined degree-degree correlations.
• Adjustable degree-dependent clustering is achieved, offering fine-grained control over network topology.
• The method demonstrates efficacy when applied to empirical network datasets.
• A complementary technique is described for setting degree-dependent clustering when correlations are predefined.

Conclusions:

• The presented algorithm offers a powerful and flexible tool for generating complex random networks.
• This advancement facilitates more accurate modeling and analysis of complex systems in science.
• The ability to tune both degree correlations and clustering enhances the applicability of random network models.