Growing networks with communities: A distributive link model

Area of Science:

• Complex systems
• Network science
• Computational biology

Background:

• The Barabasi-Albert model explains scale-free networks using evolution and popularity.
• Understanding network evolution is key to explaining the ubiquity of complex and scale-free networks.

Purpose of the Study:

• To propose a novel, simple network growth model based on the evolution principle.
• To generate modular networks with evolving communities and analyze their dynamics.
• To provide a unified explanation for regular and tree-like network communities.

Main Methods:

• Adopting the evolution principle for network growth.
• Introducing a new model with a single free parameter to control community dynamics.
• Analyzing the emergence and dominance of communities over time.

Main Results:

• The model naturally evolves modular networks with multiple, dynamically sized communities.
• Under certain conditions, the model generates tree-like networks with distinct community structures.
• New communities can mitigate the degree growth of hub nodes by absorbing link resources.
• The model demonstrates a "tyranny of the newcomer" where newer entities achieve dominance.

Conclusions:

• The proposed model offers a unified explanation for community structures in both regular and tree-like networks.
• It challenges the "early adopter" principle by showing how new nodes and communities can rise to prominence.
• The framework is applicable to real-world evolutionary networks, such as the SARS-CoV-2 haplotype network.