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.
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Notes
- 1.
For convenience, we use \(t_v\) and t(v) interchangeably to denote the threshold of a node v.
- 2.
When the system is deterministic, the successor of a configuration is unique. In probabilistic systems, the successor may not be unique.
- 3.
A complete query set Q is a set of queries q such that for every \(v \in V\) and for every value \(j \in [0,d_v+1]\), there exists a \(q \in Q\) such that score(v, q)\(=j\).
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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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Adiga, A., Kuhlman, C.J., Marathe, M.V., Ravi, S.S., Rosenkrantz, D.J., Stearns, R.E. (2019). Using Active Queries to Learn Local Stochastic Behaviors in Social Networks. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_20
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