  Structure and dynamics of complex networks usually deal with degree
distributions, clustering, shortest path lengths and other graph properties.
Although these concepts have been analysed for graphs on abstract spaces, many
networks happen to be embedded in a metric arrangement, where the geographic
distance between vertices plays a crucial role. The present work proposes a
model for growing network that takes into account the geographic distance
between vertices: the probability that they are connected is higher if they are
located nearer than farther. In this framework, the mean degree of vertices,
degree distribution and shortest path length between two randomly chosen
vertices are analysed.
