TY - GEN
T1 - Mining for geographically disperse communities in social networks by leveraging distance modularity
AU - Shakarian, Paulo
AU - Roos, Patrick
AU - Callahan, Devon
AU - Kirk, Cory
N1 - Funding Information:
P.S. would like to thank Richard M. Medina (GMU) for his help with the terrorist network dataset. The authors are supported by the Army Research Office (project 2GDATXR042) and the Office of the Secretary of Defense. The opinions in this paper are those of the authors and do not necessarily reflect the opinions of the funders, the U.S. Military Academy, or the U.S. Army.
Funding Information:
P.S. would like to thank Richard M. Medina (GMU) for his help with the terrorist network dataset. The authors are supported by the Army Research Office (project 2GDATXR042) and the Office of the Secretary of Defense. The opinions in this paper are those of the authors and do not necessarily reflect the opinions of the funders, the U.S. Military Academy, or the U.S. Army
Publisher Copyright:
Copyright © 2013 ACM.
PY - 2013/8/11
Y1 - 2013/8/11
N2 - 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 wellknown 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.
AB - 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 wellknown 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.
KW - Complex networks
KW - Geospatial reasoning
UR - http://www.scopus.com/inward/record.url?scp=84904159666&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904159666&partnerID=8YFLogxK
U2 - 10.1145/2487575.2488194
DO - 10.1145/2487575.2488194
M3 - Conference contribution
AN - SCOPUS:84904159666
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1402
EP - 1409
BT - KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
A2 - Parekh, Rajesh
A2 - He, Jingrui
A2 - Inderjit, Dhillon S.
A2 - Bradley, Paul
A2 - Koren, Yehuda
A2 - Ghani, Rayid
A2 - Senator, Ted E.
A2 - Grossman, Robert L.
A2 - Uthurusamy, Ramasamy
PB - Association for Computing Machinery
T2 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Y2 - 11 August 2013 through 14 August 2013
ER -