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Network Interdiction Model on Interdependent Incomplete Network

Xiaodan, Xie
http://orcid.org/0000-0001-5732-6851
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1593537784177702

Abstract Details

2020, Doctor of Philosophy (PhD), Ohio University, Industrial and Systems Engineering (Engineering and Technology).
The dissertation develops a methodology framework to support decision-making in effectively predicting and disrupting an interdependent incomplete network. The proposed framework is tested on the Organized Criminal Network (OCNs), i.e. interdependent sex trafficking network and the time-evolving drug trafficking network. Two models are developed for dedicated problems. First, the dissertation develops an iterative Network Interdiction Model (NIM) that can:1) Captures the interdependency of the multi-layer networks; 2) Identifies and disrupts the network with the worst damage effect. The NIM is tested in three federally prosecuted sex trafficking cases in the United States. Numerical experiments show the capability of the NIM to provide valuable direction for interdiction strategies. The study result illustrates the potential benefits of using network modeling and analytical technics in the fight against sex trafficking. Second, the dissertation develops a mathematical model capable of predicting the creation or destruction of connections in dynamic networks. The model uses a convex combination of two mechanisms: 1) link's memory, which explores the impact of historical existence on link dynamic; and 2) network topology, which explores the impact of the network topology on creation and destruction of links. The model uses a constrained maximum likelihood approach for model calibration. The estimated parameters are used to predict links; existence probability in the future. The proposed model is tested on an evolving drug trafficking network where it outperforms other baseline models. The dissertation demonstrates the importance of collecting, documenting, and analyzing the criminal network from a dynamic perspective. The study insights and predictive results can support law enforcement agents in devising a more proactive anticriminal strategy.
Felipe Aros-Vera (Advisor)
135 p.
Engineering; Industrial Engineering
interdependent network; network interdiction; criminal network; dynamic network

Recommended Citations

Citations

  • Xiaodan, X. (2020). Network Interdiction Model on Interdependent Incomplete Network [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1593537784177702

    APA Style (7th edition)

  • Xiaodan, Xie. Network Interdiction Model on Interdependent Incomplete Network . 2020. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1593537784177702.

    MLA Style (8th edition)

  • Xiaodan, Xie. "Network Interdiction Model on Interdependent Incomplete Network ." Doctoral dissertation, Ohio University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1593537784177702

    Chicago Manual of Style (17th edition)

ohiou1593537784177702
195
© 2020, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.