游戏即游戏：犯罪网络中的动态网络分析与角色变迁
The Game is the Game: Dynamic network analysis and shifting roles in criminal networks
AI总结：
本文将时间作为关键变量，采用动态Katz中心性并结合伯努利随机试验模拟缺失数据，识别犯罪网络核心参与者与角色变迁，为相关学者和执法机构提供了分析工具。
摘要：
研究目标：本文将时间作为关键变量，用于识别犯罪网络中的核心参与者，并探究行动者的位置如何随时间变化；随后结合从刑事司法记录中收集的网络数据存在的不确定性，评估上述结果的准确性。研究方法：网络数据来自针对一个贩毒分销网络开展的为期两年调查的司法文件；我们使用动态版本的Katz中心性来探究网络行动者的关系变化与相对重要性；接着采用一种新颖方法，通过伯努利随机试验向网络中引入新边，模拟缺失数据，评估在对观测到的网络引入一定程度不确定性时，基于Katz中心性的节点排名会发生多大程度的变化或保持不变。研究结果：我们识别出在为期两年的调查过程中始终处于核心角色的行动者，并将其与仅在有限时期内为集团活动提供关键贡献的行动者区分开；结果表明，与犯罪网络分析中常用的中心性度量相比，动态Katz中心性即便在数据不完整时，也有助于区分核心节点间的个体贡献，并探究个体随时间的轨迹。研究结论：本文证明了使用时间网络数据识别核心参与者的价值，为试图捕捉犯罪协作复杂性质的有组织犯罪学者，以及旨在识别合适目标并瓦解犯罪集团的执法机构，提供了一种额外的分析工具。
英文摘要：
Objectives: This paper incorporates time as a crucial variable to identify key players in criminal networks and explores how actors' positions change over time. It then assesses the accuracy of the results against the uncertainty around network data collected from criminal justice records. Methods: Network data are from a judicial document for a two-year investigation targeting a drug trafficking and distribution network. We use Katz centrality in its dynamic version to explore changes in relationships and relative importance of network actors. We then use a novel method of introducing new edges to the network using Bernoulli random trials to simulate missing data and assess the extent to which node rankings based on Katz centrality change or remain the same when introducing some level of uncertainty to our observed network. Results: We identify actors who consistently held a central role over the course of the two-year investigation and differentiate them from actors who provided key contributions to the group's activities, but only for a limited period. We show that compared to centrality measures commonly used in criminal network analysis, dynamic Katz centrality is helpful to differentiate individual contributions even among central nodes and explore individual trajectories over time, even when data are incomplete. Conclusions: This paper demonstrates the value of key player identification using temporal network data and offers an additional analytical tool to both organised crime scholars trying to capture the complex nature of criminal collaboration and law enforcement agencies aiming at identifying appropriate targets and disrupting criminal groups.
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。
