  Social networks where the actors occupy geospatial locations are prevalent in
military, intelligence, and policing operations such as counter-terrorism,
counter-insurgency, and combating organized crime. These networks are often
derived from a variety of intelligence sources. The discovery of communities
that are geographically disperse stems from the requirement to identify
higher-level organizational structures, such as a logistics group that provides
support to various geographically disperse terrorist cells. We apply a variant
of Newman-Girvan modularity to this problem known as distance modularity. To
address the problem of finding geographically disperse communities, we modify
the well-known Louvain algorithm to find partitions of networks that provide
near-optimal solutions to this quantity. We apply this algorithm to numerous
samples from two real-world social networks and a terrorism network data set
whose nodes have associated geospatial locations. Our experiments show this to
be an effective approach and highlight various practical considerations when
applying the algorithm to distance modularity maximization. Several military,
intelligence, and law-enforcement organizations are working with us to further
test and field software for this emerging application.
