垃圾进，垃圾出：数据质量对犯罪网络干预的影响
Garbage in Garbage out: Impacts of data quality on criminal network intervention
AI总结：
本文结合图论与机器学习方法评估数据质量对犯罪网络干预效果的影响，发现去中心化和简单启发式会显著增强网络韧性，并据此提出谨慎应用网络科学、建设互操作情报生态与新型网络推断技术。
摘要：
诸如人口贩运团伙之类的犯罪网络对全球社会的法治、民主和公共安全构成威胁。网络科学为执法机构（LEAs）识别关键参与者并设计干预措施（例如瓦解其组织）提供了宝贵工具。然而，糟糕的数据质量以及犯罪网络通过自组织表现出的适应性，使有效破坏变得极具挑战性。尽管已有大量工作构建并应用网络科学工具来打击犯罪网络，但这些工作往往隐含地假设网络测量是准确且完整的。此外，迄今为止尚缺乏对数据质量如何影响下游干预效果的全面理解。本研究基于经典图论方法和基于机器学习的方法，考察数据质量与干预有效性之间的关系。去中心化成为网络韧性的主要因素，尤其是在数据不完整的条件下，这会使攻击策略在很大程度上失效。此外，集中式网络的韧性可通过简单的启发式方法得到增强，从而使定向攻击更加不可行。因此，我们主张在破坏犯罪网络时更谨慎地应用网络科学，持续发展可互操作的情报生态系统，并创建新型网络推断技术以应对数据质量挑战。
英文摘要：
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 adaptiveness of criminal networks through self-organization make effective disruption extremely challenging. Although there exists a large body of work building and applying network scientific tools to attack criminal networks, these work often implicitly assume that the network measurements are accurate and complete. 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 approaches. Decentralization emerges as a major factor in network robustness, particularly under conditions of incomplete data, which renders attack strategies largely ineffective. Moreover, the robustness of centralized networks can be boosted using simple heuristics, making targeted attack 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
