Holme and Kim algorithm for growing graphs with powerlaw degree distribution and approximate average clustering.
Parameters :  n : int
m : int
p : float,
seed : int, optional


Notes
The average clustering has a hard time getting above a certain cutoff that depends on m. This cutoff is often quite low. Note that the transitivity (fraction of triangles to possible triangles) seems to go down with network size.
It is essentially the BarabásiAlbert (BA) growth model with an extra step that each random edge is followed by a chance of making an edge to one of its neighbors too (and thus a triangle).
This algorithm improves on BA in the sense that it enables a higher average clustering to be attained if desired.
It seems possible to have a disconnected graph with this algorithm since the initial m nodes may not be all linked to a new node on the first iteration like the BA model.
References
[R238]  P. Holme and B. J. Kim, “Growing scalefree networks with tunable clustering”, Phys. Rev. E, 65, 026107, 2002. 