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

Paulo Shakarian, Patrick Roos, Devon Callahan, Cory Kirk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

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 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.

Original languageEnglish (US)
Title of host publicationKDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1402-1409
Number of pages8
VolumePart F128815
ISBN (Electronic)9781450321747
DOIs
StatePublished - Aug 11 2013
Externally publishedYes
Event19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States
Duration: Aug 11 2013Aug 14 2013

Other

Other19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
CountryUnited States
CityChicago
Period8/11/138/14/13

Fingerprint

Terrorism
Crime
Law enforcement
Logistics
Experiments

Keywords

  • Complex networks
  • Geospatial reasoning

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Shakarian, P., Roos, P., Callahan, D., & Kirk, C. (2013). Mining for geographically disperse communities in social networks by leveraging distance modularity. In KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. Part F128815, pp. 1402-1409). [2488194] Association for Computing Machinery. https://doi.org/10.1145/2487575.2488194

Mining for geographically disperse communities in social networks by leveraging distance modularity. / Shakarian, Paulo; Roos, Patrick; Callahan, Devon; Kirk, Cory.

KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F128815 Association for Computing Machinery, 2013. p. 1402-1409 2488194.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shakarian, P, Roos, P, Callahan, D & Kirk, C 2013, Mining for geographically disperse communities in social networks by leveraging distance modularity. in KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. Part F128815, 2488194, Association for Computing Machinery, pp. 1402-1409, 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, United States, 8/11/13. https://doi.org/10.1145/2487575.2488194
Shakarian P, Roos P, Callahan D, Kirk C. Mining for geographically disperse communities in social networks by leveraging distance modularity. In KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F128815. Association for Computing Machinery. 2013. p. 1402-1409. 2488194 https://doi.org/10.1145/2487575.2488194
Shakarian, Paulo ; Roos, Patrick ; Callahan, Devon ; Kirk, Cory. / Mining for geographically disperse communities in social networks by leveraging distance modularity. KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F128815 Association for Computing Machinery, 2013. pp. 1402-1409
@inproceedings{897016b23b0b433c8a5cdc144964fa0c,
title = "Mining for geographically disperse communities in social networks by leveraging distance modularity",
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 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.",
keywords = "Complex networks, Geospatial reasoning",
author = "Paulo Shakarian and Patrick Roos and Devon Callahan and Cory Kirk",
year = "2013",
month = "8",
day = "11",
doi = "10.1145/2487575.2488194",
language = "English (US)",
volume = "Part F128815",
pages = "1402--1409",
booktitle = "KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",

}

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

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

VL - Part F128815

SP - 1402

EP - 1409

BT - KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

ER -