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

TL;DR

This paper introduces a modified Louvain algorithm to detect geographically dispersed communities in social networks using distance modularity, aiding military and law enforcement in organizational analysis.

Contribution

We adapt the Louvain algorithm to optimize distance modularity for identifying dispersed communities, demonstrating its effectiveness on real-world geospatial social network data.

Findings

Effective detection of geographically dispersed communities

Algorithm performs well on real-world data sets

Practical considerations for applying distance modularity maximization

Abstract

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.