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Unifying Evolutionary and Network Dynamics
Authors: Samarth Swarup, Les Gasser
(Submitted on 13 Apr 2007)
Abstract: Many important real-world networks manifest "small-world" properties such as scale-free degree distributions, small diameters, and clustering. The most common model of growth for these networks is "preferential attachment", where nodes acquire new links with probability proportional to the number of links they already have. We show that preferential attachment is a special case of the process of molecular evolution. We present a new single-parameter model of network growth that unifies varieties of preferential attachment with the quasispecies equation (which models molecular evolution), and also with the Erdos-Renyi random graph model. We suggest some properties of evolutionary models that might be applied to the study of networks. We also derive the form of the degree distribution resulting from our algorithm, and we show through simulations that the process also models aspects of network growth. The unification allows mathematical machinery developed for evolutionary dynamics to be applied in the study of network dynamics, and vice versa.
Comments:
11 pages, 12 figures, Accepted for publication in Physical Review E
Subjects:
Quantitative Methods (q-bio.QM); Populations and Evolution (q-bio.PE)
Cite as:
arXiv:0704.1811v1 [q-bio.QM]
Authors: Samarth Swarup, Les Gasser
(Submitted on 13 Apr 2007)
Abstract: Many important real-world networks manifest "small-world" properties such as scale-free degree distributions, small diameters, and clustering. The most common model of growth for these networks is "preferential attachment", where nodes acquire new links with probability proportional to the number of links they already have. We show that preferential attachment is a special case of the process of molecular evolution. We present a new single-parameter model of network growth that unifies varieties of preferential attachment with the quasispecies equation (which models molecular evolution), and also with the Erdos-Renyi random graph model. We suggest some properties of evolutionary models that might be applied to the study of networks. We also derive the form of the degree distribution resulting from our algorithm, and we show through simulations that the process also models aspects of network growth. The unification allows mathematical machinery developed for evolutionary dynamics to be applied in the study of network dynamics, and vice versa.
Comments:
11 pages, 12 figures, Accepted for publication in Physical Review E
Subjects:
Quantitative Methods (q-bio.QM); Populations and Evolution (q-bio.PE)
Cite as:
arXiv:0704.1811v1 [q-bio.QM]
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