Abstract
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. We focus on the regime where the hidden variables follow a power law with exponent , so that the degrees have infinite variance. The natural cutoff characterizes the largest degrees in the hidden variable models, and a structural cutoff introduces negative degree correlations (disassortative mixing) due to the infinite-variance degrees. 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 this gives for the universality class at hand. We characterize the extremely slow decay of when and show that for , say, clustering starts to vanish only for networks as large as .
- Received 11 November 2016
DOI:https://doi.org/10.1103/PhysRevE.95.022307
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