Seeing red: Locating people of interest in networks

Pivithuru Wijegunawardana, Vatsal Ojha, Ralucca Gera, Sucheta Soundarajan

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSpringer Proceedings in Complexity
PublisherSpringer
Pages141-150
Number of pages10
VolumePart F2
DOIs
StatePublished - Jan 1 2017

Keywords

  • Lying scenarios
  • Multilayered networks
  • Nodes of interest
  • Sampling

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications

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  • Cite this

    Wijegunawardana, P., Ojha, V., Gera, R., & Soundarajan, S. (2017). Seeing red: Locating people of interest in networks. In Springer Proceedings in Complexity (Vol. Part F2, pp. 141-150). Springer. https://doi.org/10.1007/978-3-319-54241-6_12