  The main problem we address in this paper is whether function determines form
when a society of agents organizes itself for some purpose or whether the
organizing method is more important than the functionality in determining the
structure of the ensemble. As an example, we use a neural network that learns
the same function by two different learning methods. For sufficiently large
networks, very different structures may indeed be obtained for the same
functionality. Clustering, characteristic path length and hierarchy are
structural differences, which in turn have implications on the robustness and
adaptability of the networks. In networks, as opposed to simple graphs, the
connections between the agents are not necessarily symmetric and may have
positive or negative signs. New characteristic coefficients are introduced to
characterize this richer connectivity structure.
