  We analyze a distributed information network in which each node has access to
the information contained in a limited set of nodes (its neighborhood) at a
given time. A collective computation is carried out in which each node
calculates a value that implies all information contained in the network (in
our case, the average value of a variable that can take different values in
each network node). The neighborhoods can change dynamically by exchanging
neighbors with other nodes. The results of this collective calculation show
rapid convergence and good scalability with the network size. These results are
compared with those of a fixed network arranged as a square lattice, in which
the number of rounds to achieve a given accuracy is very high when the size of
the network increases. The results for the evolving networks are interpreted in
light of the properties of complex networks and are directly relevant to the
diameter and characteristic path length of the networks, which seem to express
"small world" properties.
