Analytical approach to bond percolation on clustered networks

Abstract

An analytical approach to calculating bond percolation thresholds and sizes of giant connected components on random networks with non-zero clustering is presented. The networks are generated using a generalization of Trapman's [P. Trapman, Theor. Pop. Biol. 71, 160 (2007)] model of cliques embedded in tree-like random graphs. The resulting networks have arbitrary degree distributions and tunable degree-dependent clustering. The effect of clustering on the percolation thresholds is examined and contrasted with some recent results in the literature. © 2009 Springer-Verlag Berlin Heidelberg.