Seeing Red: Locating People of Interest in Networks
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Authors
Wijegunawardana, Pivithuru
Ojha, Vatsal
Gera, Ralucca
Soundarajan, Sucheta
Subjects
multilayered networks
sampling
lying scenarios
nodes of interest
sampling
lying scenarios
nodes of interest
Advisors
Date of Issue
2017
Date
2017
Publisher
Springer
Language
Abstract
The focus of the current research is to identify people of interest in
social networks. We are especially interested in studying dark networks, which
represent illegal or covert activity. In such networks, people are unlikely to disclose
accurate information when queried. We present REDLEARN, an algorithm
for sampling dark networks with the goal of identifying as many nodes of interest
as possible. We consider two realistic lying scenarios, which describe how individuals
in a dark network may attempt to conceal their connections. We test and
present our results on several real-world multilayered networks, and show that
REDLEARN achieves up to a 340% improvement over the next best strategy.
Type
Article
Description
Series/Report No
Department
Applied Mathematics
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
DoD
Funder
Format
8 p.
Citation
Wijegunawardana, Pivithuru, Vatsal Ojha, Ralucca Gera, and Sucheta Soundarajan. "Seeing red: Locating people of interest in networks." In Workshop on Complex Networks CompleNet, pp. 141-150. Springer, Cham, 2017.
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.