Survivor statistics and damage spreading on social network with power-law degree distributions

Damage spreading(DS) of the random graph networks with power-law degree distributions is investigated using Glauber dynamics. Various subgraphs defined by the probability of acquaintance show distinct features in DS as measured by Hamming distance. A heuristic understanding of the long-time value of damage is achieved through an analysis of the survivor statistics. All survivors are dynamical, flipping in unison for the controlled sample and the damaged sample. Verification of these dynamic survivors is achieved through the introduction of a new measure of self-damage.