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Using Active Queries to Learn Local Stochastic Behaviors in Social Networks

  • Abhijin Adiga
  • Chris J. Kuhlman
  • Madhav V. Marathe
  • S. S. Ravi
  • Daniel J. Rosenkrantz
  • Richard E. Stearns
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

Using a stochastic synchronous dynamical system (SyDS) as a formal model, we study the problem of inferring local behaviors of nodes in networked social systems. We focus on probabilistic threshold functions as local functions. We use an active query mechanism where a user interacts with the system by submitting queries. We develop an efficient algorithm that infers the probabilistic threshold functions using the responses to the queries. Our algorithm generates provably good query sets. We also present experimental results to demonstrate the performance of our algorithm.

Notes

Acknowledgements

We thank the referees for providing valuable suggestions. We also thank our computer systems administrators for their help: Dominik Borkowski, William Miles Gentry, Jeremy Johnson, William Marmagas, Douglas McMaster, Kevin Shinpaugh, and Robert Wills. This work has been partially supported by DARPA Cooperative Agreement D17AC00003 (NGS2), DTRA CNIMS (Contract HDTRA1-11-D-0016-0001), NSF DIBBS Grant ACI-1443054 and NSF BIG DATA Grant IIS-1633028.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abhijin Adiga
    • 1
  • Chris J. Kuhlman
    • 1
  • Madhav V. Marathe
    • 2
  • S. S. Ravi
    • 2
    • 3
  • Daniel J. Rosenkrantz
    • 4
  • Richard E. Stearns
    • 4
  1. 1.Virginia TechBlacksburgUSA
  2. 2.University of VirginiaCharlottesvilleUSA
  3. 3.University at Albany – SUNYAlbanyUSA
  4. 4.University at Albany – SUNYAlbanyUSA

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