  We investigate some of the properties and extensions of a dynamic innovation
network model recently introduced in
\citep{koenig07:_effic_stabil_dynam_innov_networ}. In the model, the set of
efficient graphs ranges, depending on the cost for maintaining a link, from the
complete graph to the (quasi-) star, varying within a well defined class of
graphs. However, the interplay between dynamics on the nodes and topology of
the network leads to equilibrium networks which are typically not efficient and
are characterized, as observed in empirical studies of R&D networks, by
sparseness, presence of clusters and heterogeneity of degree. In this paper, we
analyze the relation between the growth rate of the knowledge stock of the
agents from R&D collaborations and the properties of the adjacency matrix
associated with the network of collaborations. By means of computer simulations
we further investigate how the equilibrium network is affected by increasing
the evaluation time $\tau$ over which agents evaluate whether to maintain a
link or not. We show that only if $\tau$ is long enough, efficient networks can
be obtained by the selfish link formation process of agents, otherwise the
equilibrium network is inefficient. This work should assist in building a
theoretical framework of R&D networks from which policies can be derived that
aim at fostering efficient innovation networks.
