Growing networks with communities: A distributive link model

• Published in: Chaos (Woodbury, N.Y.) N.Y.), 2020-04, Vol.30 (4), p.041101-041101
• Main Authors: Shang, Ke-ke, Yang, Bin, Moore, Jack Murdoch, Ji, Qian, Small, Michael
• Format: Article
• Language: English
• Subjects: Algorithms; Biological Evolution; Community Networks; Evolution; Fast Track; Humans; Models, Theoretical; Networks; Nodes; Severe acute respiratory syndrome coronavirus 2
• ISSN: 1054-1500; 1089-7682
• DOI: 10.1063/5.0007422

Evolution and popularity are two keys of the Barabasi–Albert model, which generates a power law distribution of network degrees. Evolving network generation models are important as they offer an explanation of both how and why complex networks (and scale-free networks, in particular) are ubiquitous. We adopt the evolution principle and then propose a very simple and intuitive new model for network growth, which naturally evolves modular networks with multiple communities. The number and size of the communities evolve over time and are primarily subjected to a single free parameter. Surprisingly, under some circumstances, our framework can construct a tree-like network with clear community structures—branches and leaves of a tree. Results also show that new communities will absorb a link resource to weaken the degree growth of hub nodes. Our models have a common explanation for the community of regular and tree-like networks and also breaks the tyranny of the early adopter; unlike the standard popularity principle, newer nodes and communities will come to dominance over time. Importantly, our model can fit well with the construction of the SARS-Cov-2 haplotype evolutionary network.