Sampling dark networks to locate people of interest
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Authors
Wijegunawardana, Pivithuru
Ojha, Vatsal
Gera, Ralucca
Soundarajan, Sucheta
Subjects
Sampling
Lying scenarios
Nodes of interest
Dark network
Lying scenarios
Nodes of interest
Dark network
Advisors
Date of Issue
2018
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
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.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.1007/s13278-018-0487-0
Series/Report No
Department
Applied Mathematics
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
27 p.
Citation
Wijegunawardana, Pivithuru, et al. "Sampling dark networks to locate people of interest." Social Network Analysis and Mining 8.1 (2018): 15.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.