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Sampling dark networks to locate people of interest

  • Pivithuru WijegunawardanaEmail author
  • Vatsal Ojha
  • Ralucca Gera
  • Sucheta Soundarajan
Original Article
  • 183 Downloads

Abstract

Dark networks, which describe networks with covert entities and connections such as those representing illegal activities, are of great interest to intelligence analysts. However, before studying such a network, one must first collect appropriate network data. Collecting accurate network data in such a setting is a challenging task, as data collectors will make inferences, which may be incorrect, based on available intelligence data, which may itself be misleading. In this paper, we consider the problem of how to effectively sample dark networks, in which sampling queries may return incorrect information, with the specific goal of locating people of interest. We present RedLearn and RedLearnRS, two algorithms for crawling dark networks with the goal of maximizing the identification of nodes of interest, given a limited sampling budget. RedLearn assumes that a query on a node can accurately return whether a node represents a person of interest, while RedLearnRS dispenses with that assumption. We consider realistic error scenarios, which describe how individuals in a dark network may attempt to conceal their connections. We evaluate and present results on several real-world networks, including dark networks, as well as various synthetic dark network structures proposed in the criminology literature. Our analysis shows that RedLearn and RedLearnRS meet or outperform other sampling strategies.

Keywords

Sampling Lying scenarios Nodes of interest Dark networks 

Notes

Acknowledgements

R. Gera thanks the DoD for partially sponsoring this work. This research was supported in part through computational resources provided by Syracuse University.

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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA
  2. 2.Science and Humanities Scholars ProgramCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of Applied MathematicsNaval Postgraduate SchoolMontereyUSA

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