Journal article
Garbage in garbage out? Impacts of data quality on criminal network intervention
- Abstract:
- Criminal networks such as human trafficking rings are threats to the rule of law, democracy and public safety in our global society. Network science provides invaluable tools to identify key players and design interventions for Law Enforcement Agencies (LEAs), e.g., to dismantle their organisation. However, poor data quality and the robustness of criminal networks make effective intervention extremely challenging. Although there exists a large body of work building and applying network scientific tools to green intervene criminal networks, these work often neglect the problems of data incompleteness and inaccuracy. Moreover, there is thus far no comprehensive understanding of the impacts of data quality on the downstream effectiveness of interventions. This work investigates the relationship between data quality and intervention effectiveness based on classical graph theoretic and machine learning-based targeting approaches. Decentralization emerges as a major factor in network robustness, particularly under conditions of incomplete data, which renders intervention strategies largely ineffective. Moreover, the robustness of centralized networks can be boosted using simple heuristics, making targeted intervention more infeasible. Consequently, we advocate for a more cautious application of network science in disrupting criminal networks, the continuous development of an interoperable intelligence ecosystem, and the creation of novel network inference techniques to address data quality challenges.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.9MB, Terms of use)
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- Publisher copy:
- 10.1140/epjds/s13688-025-00553-x
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/V013068/1
- EP/V03474X/1
- EP/Y028872/1
- Publisher:
- Springer Nature
- Journal:
- EPJ Data Science More from this journal
- Volume:
- 14
- Issue:
- 1
- Article number:
- 37
- Publication date:
- 2025-05-12
- Acceptance date:
- 2025-04-27
- DOI:
- EISSN:
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2193-1127
- Language:
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English
- Keywords:
- Pubs id:
-
2094221
- Local pid:
-
pubs:2094221
- Deposit date:
-
2025-06-10
- ARK identifier:
Terms of use
- Copyright holder:
- Yeung et al
- Copyright date:
- 2025
- Rights statement:
- ©2025 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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