English

Percolation on a maximally disassortative network

Physics and Society 2020-02-11 v2 Statistical Mechanics

Abstract

We propose a maximally disassortative (MD) network model which realizes a maximally negative degree-degree correlation, and study its percolation transition to discuss the effect of a strong degree-degree correlation on the percolation critical behaviors. Using the generating function method for bipartite networks, we analytically derive the percolation threshold and the order parameter critical exponent, β\beta. For the MD scale-free networks, whose degree distribution is P(k)kγP(k) \sim k^{-\gamma}, we show that the exponent, β\beta, for the MD networks and corresponding uncorrelated networks are same for γ>3\gamma>3 but are different for 2<γ<32<\gamma<3. A strong degree-degree correlation significantly affects the percolation critical behavior in heavy-tailed scale-free networks. Our analytical results for the critical exponents are numerically confirmed by a finite-size scaling argument.

Keywords

Cite

@article{arxiv.1905.08466,
  title  = {Percolation on a maximally disassortative network},
  author = {Shogo Mizutaka and Takehisa Hasegawa},
  journal= {arXiv preprint arXiv:1905.08466},
  year   = {2020}
}

Comments

7 pages, 3 figures

R2 v1 2026-06-23T09:14:40.888Z