Skip to main content

Leveraging Advances in Social Network Thinking for National Security Proceedings of a Workshop (2018) / Chapter Skim
Currently Skimming:

4 Multilevel, High-Dimensional, Evolving, and Emerging Networks
Pages 29-36

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 29...
... EXPLORING DARK NETWORKS Hsinchun Chen, University of Arizona, addressed the topic of "dark networks," a term that refers to illegal and covert networks. His work on dark networks has encompassed gang and narcotic networks, extremist and terrorist networks, and computer hackers.
Read the entire page →
From page 30...
... The system, he said, collects cross-jurisdictional information from multiple databases. A license plate reader captures a vehicle's license plate information and within seconds, the data mining tool generates associations between the license plate and other types of information, such as the vehicle's owner and other Department of Motor Vehicles information, police records, and the context of the crossing (e.g., day, time, other vehicles)
Read the entire page →
From page 31...
... He suggested further that advanced tools could improve the comprehensive and timely collection of open-source information; that AI could assist with entity and relationship recognition; and that advanced data analytics could expand research opportunities. Finally, he cited research on adversarial machine learning1 as an area potentially poised for advancement.
Read the entire page →
From page 32...
... Bad models, on the other hand, are highly combinatorial and algorithmic, and often entail black box operations in which the applied theory of social process is unclear. Golub suggested a focus on useful decompositions as a way to develop success 2 Dell, M
Read the entire page →
From page 33...
... Expanding on this thought, he offered the analogy of functional magnetic resonance imaging experiments and suggested that useful science will test interventions so as to "shine something at the system [or network] and see how it vibrates." THE FUTURE OF COMPLEX NETWORKS Alexander Volfovsky, Duke University, spoke about the challenges of using statistical techniques to study causal relationships within networks.
Read the entire page →
From page 34...
... to illustrate the limitations of simple models and the need for developing models that directly account for the data collection process. As part of the AddHealth study, American high school students were asked to identify their top five friends.
Read the entire page →
From page 35...
... In closing, Volfovsky suggested three areas that need to be addressed in the near future: substantive network challenges (i.e., better understanding of positions, relationships, and trigger points in a network) ; statistical techniques for addressing uncertainties in observed networks; and engineering solutions to the current computational expense of available models.
Read the entire page →

Key Terms

  • dark networks
  • data mining
  • license plate
  • five friends
  • network characteristics

  • mining tool
  • trafficking networks
  • terrorist networks
  • tool draws
  • dark web

  • statistical techniques
  • causal relationships
  • computer hackers
  • police records
  • machine learning

  • detecting communities
  • game theory
  • robust statistical
  • study using
  • network links

  • gested network
  • network components
  • using statistical
  • extensive data
  • adaptive networks

  • data science
  • similar data
  • network challenges
  • connect network
  • ary research


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
    More information on Chapter Skim is available.