  In this paper we present a generalized configuration model with random
triadic closure (GCTC). This model possesses five fundamental properties: large
clustering coefficient, power law degree distribution, short path length,
non-zero Pearson degree correlation, and existence of community structures. We
analytically derive the Pearson degree correlation coefficient and the
clustering coefficient of the proposed model. We select a few datasets of
real-world networks. By simulation, we show that the GCTC model matches very
well with the datasets in terms of Pearson degree correlations and clustering
coefficients. We also test three well-known community detection algorithms on
our model, the datasets and other three prevalent benchmark models. We show
that the GCTC model performs equally well as the other three benchmark models.
Finally, we perform influence diffusion on the GCTC model using the independent
cascade model and the linear threshold model. We show that the influence
spreads of the GCTC model are much closer to those of the datasets than the
other benchmark models. This suggests that the GCTC model is a suitable tool to
study network science problems where degree correlation or clustering plays an
important role.
