  Many complex systems are organized in the form of a network embedded in
space. Important examples include the physical Internet infrastucture, road
networks, flight connections, brain functional networks and social networks.
The effect of space on network topology has recently come under the spotlight
because of the emergence of pervasive technologies based on geo-localization,
which constantly fill databases with people's movements and thus reveal their
trajectories and spatial behaviour. Extracting patterns and regularities from
the resulting massive amount of human mobility data requires the development of
appropriate tools for uncovering information in spatially-embedded networks. In
contrast with most works that tend to apply standard network metrics to any
type of network, we argue in this paper for a careful treatment of the
constraints imposed by space on network topology. In particular, we focus on
the problem of community detection and propose a modularity function adapted to
spatial networks. We show that it is possible to factor out the effect of space
in order to reveal more clearly hidden structural similarities between the
nodes. Methods are tested on a large mobile phone network and
computer-generated benchmarks where the effect of space has been incorporated.
