• DOI:10.1145/2487575.2488194
Corpus ID: 110149

Mining for geographically disperse communities in social networks by leveraging distance modularity

@article{Shakarian2013MiningFG,
  title={Mining for geographically disperse communities in social networks by leveraging distance modularity},
  author={P. Shakarian and P. Roos and Devon Callahan and C. Kirk},
  journal={Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining},
  year={2013}
}

P. Shakarian, P. Roos, +1 author C. Kirk

Published 2013

Computer Science, Physics

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

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… CONTINUE READING

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