  Stochasticity is introduced to a well studied class of recursively grown
graphs: $(u,v)$-flower nets, which have power-law degree distributions as well
as small-world properties (when $u=1$). The stochastic variant interpolates
between different (deterministic) flower graphs and might better model
real-world networks. The random multiplicative growth process involved,
however, leads to a spread ensemble of networks with finite variance for the
number of links, nodes, and loops. Nevertheless, the degree exponent and
loopiness exponent attain unique values in the thermodynamic limit of
infinitely large graphs. We also study a class of mixed flower networks,
closely related to the stochastic flowers, but which are grown recursively in a
deterministic way. The deterministic growth of mixed flower-nets eliminates
ensemble spreads, and their recursive growth allows for exact analysis of their
(uniquely defined) mixed properties.
