We investigate the presence of triangles in a class of correlated random graphs in which hidden variables determine the pairwise connections between vertices. The class rules out self-loops and multiple edges and allows for negative degree correlations (disassortative mixing) due to infinite-variance degrees controlled by a structural cutoff and natural cutoff . We show that local clustering decreases with the hidden variable (or degree). We also determine how the average clustering coefficient scales with the network size , as a function of and . For scale-free networks with exponent and the default choices and $h_c\sim N^{1/(\tau-1)}C\sim N^{2-\tau}\ln N$ for the universality class at hand. We characterize the extremely slow decay of when and show that for , say, clustering only starts to vanish for networks as large as .

生成讲解博客的中文版本

生成AI智能校验的中文版本