  Real social networks are often compared to random graphs in order to assess
whether their typological structure could be the result of random processes.
However, an Erd\H{o}s-R\'enyi random graph in large scale is often lack of
local structure beyond the dyadic level and as a result we need to generate the
clustered random graph instead of the simple random graph to compare the local
structure at the triadic level. In this paper a generalized version of
Gleeson's algorithm is advanced to generate a clustered random graph in
large-scale which persists the number of nodes |V|, the number of edges |E|,
and the global clustering coefficient C{\Delta} as in the real network. And it
also has advantages in randomness evaluation and computation time when
comparing with the existing algorithms.
