English

On two biased graph processes

Combinatorics 2007-05-23 v1 Analysis of PDEs Probability

Abstract

In [Amir et al.], the authors consider the generalization \Gor\Gor of the Erd\H{o}s-R\'enyi random graph process GG, where instead of adding new edges uniformly, \Gor\Gor gives a weight of size 1 to missing edges between pairs of isolated vertices, and a weight of size K[0,)K\in[0,\infty) otherwise. This can correspond to the linking of settlements or the spreading of an epidemic. The authors investigate \tgor(K)\tgor(K), the critical time for the appearance of a giant component as a function of KK, and prove that \tgor=(1+o(1))43K\tgor=(1+o(1))\frac{4}{\sqrt{3K}}, using a proper timescale. In this work, we show that a natural variation of the model \Gor\Gor has interesting properties. Define the process \Gand\Gand, where a weight of size KK is assigned to edges between pairs of non-isolated vertices, and a weight of size 1 otherwise. We prove that the asymptotical behavior of the giant component threshold is essentially the same for \Gand\Gand, and namely \tgand/\tgor\tgand / \tgor tends to 646π(24+π2)1.47\frac{64\sqrt{6}}{\pi(24+\pi^2)}\approx 1.47 as KK\to\infty. However, the corresponding thresholds for connectivity satisfy \tcand/\tcor=max{1/2,K}\tcand / \tcor=\max\{{1/2},K\} for every K>0K>0. Following the methods of [Amir et al.], \tgand\tgand is characterized as the singularity point to a system of differential equations, and computer simulations of both models agree with the analytical results as well as with the asymptotic analysis. In the process, we answer the following question: when does a giant component emerge in a graph process where edges are chosen uniformly out of all edges incident to isolated vertices, while such exist, and otherwise uniformly? This corresponds to the value of \tgand(0)\tgand(0), which we show to be 3/2+43e21{3/2}+\frac{4}{3\mathrm{e}^2-1}.

Keywords

Cite

@article{arxiv.math/0608097,
  title  = {On two biased graph processes},
  author = {Gideon Amir and Eyal Lubetzky},
  journal= {arXiv preprint arXiv:math/0608097},
  year   = {2007}
}