  We show how the combined use of the free software packages networkX and
NetLogo allows to implement quickly and with large flexibility agent-based
network simulations of the classical Bass diffusion model and of its extensions
and modifications. In addition to the standard internal graph implementations
available in NetLogo (random, Barabasi-Albert-1 and small world), one can thus
employ more complex Barabasi-Albert and small-world networks, plus scale-free
networks with arbitrary power-law exponent $\gamma$ built in networkX through a
configuration model algorithm. It is also possible to induce degree
correlations in the networks in a controlled way via Newman rewiring and to
simulate dynamics on arbitrary signed networks (networks where link can have
positive or negative weights, with corresponding effects on diffusion). Some
new results obtained in the agent-based simulations (and differing from those
in mean-field approximation) are the following. The introduction of assortative
correlations in scale-free networks has the effect of delaying the adoption
peak in the Bass model, compared to the uncorrelated case. The peak time
$t_{max}$ depends strongly also on the maximum degree effectively present in
the network. For diffusion models with threshold on signed network, if negative
influences have a weight equal to or greater than positive influences, then a
high level of clustering tends to cause adoption blockades. In connection to
this, by analysing statistical ensembles of assortative scale-free networks
generated via Newman rewiring one observes a remarkable strong correlation
between the function of the average degree of first neighbors $\bar{k}_{nn}(k)$
and the average clustering coefficient depending on the degree $\bar{C}(k)$.
