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Social networks where the actors occupy geospatial locations are prevalent inmilitary, intelligence, and policing operations such as counter-terrorism,counter-insurgency, and combating organized crime. These networks are oftenderived from a variety of intelligence sources. The discovery of communitiesthat are geographically disperse stems from the requirement to identifyhigher-level organizational structures, such as a logistics group that providessupport to various geographically disperse terrorist cells. We apply a variantof Newman-Girvan modularity to this problem known as distance modularity. Toaddress the problem of finding geographically disperse communities, we modifythe well-known Louvain algorithm to find partitions of networks that providenear-optimal solutions to this quantity. We apply this algorithm to numeroussamples from two real-world social networks and a terrorism network data setwhose nodes have associated geospatial locations. Our experiments show this tobe an effective approach and highlight various practical considerations whenapplying the algorithm to distance modularity maximization. Several military,intelligence, and law-enforcement organizations are working with us to furthertest and field software for this emerging application.

Mining for Geographically Disperse Communities in Social Networks by Leveraging Distance Modularity