  We study spatial embeddings of random graphs in which nodes are randomly
distributed in geographical space. We let the edge probability between any two
nodes to be dependent on the spatial distance between them and demonstrate that
this model captures many generic properties of social networks, including the
``small-world'' properties, skewed degree distribution, and most distinctively
the existence of community structures.
