On the concentration of the maximum degree in the duplication-divergence models
(2308.04336)Abstract
We present a rigorous and precise analysis of the maximum degree and the average degree in a dynamic duplication-divergence graph model introduced by Sol\'e, Pastor-Satorras et al. in which the graph grows according to a duplication-divergence mechanism, i.e. by iteratively creating a copy of some node and then randomly alternating the neighborhood of a new node with probability $p$. This model captures the growth of some real-world processes e.g. biological or social networks. In this paper, we prove that for some $0 < p < 1$ the maximum degree and the average degree of a duplication-divergence graph on $t$ vertices are asymptotically concentrated with high probability around $tp$ and $\max{t{2 p - 1}, 1}$, respectively, i.e. they are within at most a polylogarithmic factor from these values with probability at least $1 - t{-A}$ for any constant $A > 0$.
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