English

Graph Partitioning Induced Phase Transitions

Statistical Mechanics 2007-10-07 v1 Disordered Systems and Neural Networks

Abstract

We study the percolation properties of graph partitioning on random regular graphs with N vertices of degree kk. Optimal graph partitioning is directly related to optimal attack and immunization of complex networks. We find that for any partitioning process (even if non-optimal) that partitions the graph into equal sized connected components (clusters), the system undergoes a percolation phase transition at f=fc=12/kf=f_c=1-2/k where ff is the fraction of edges removed to partition the graph. For optimal partitioning, at the percolation threshold, we find SN0.4S \sim N^{0.4} where SS is the size of the clusters and N0.25\ell\sim N^{0.25} where \ell is their diameter. Additionally, we find that SS undergoes multiple non-percolation transitions for f<fcf<f_c.

Keywords

Cite

@article{arxiv.cond-mat/0702417,
  title  = {Graph Partitioning Induced Phase Transitions},
  author = {Gerald Paul and Reuven Cohen and Sameet Sreenivasan and Shlomo Havlin and H. Eugene Stanley},
  journal= {arXiv preprint arXiv:cond-mat/0702417},
  year   = {2007}
}