MULTI-CRITERIA DECISION SUPPORT TO CRIMINOLOGY BY GRAPH THEORY AND COMPOSITION OF PROBABILISTIC PREFERENCES

ABSTRACT This study associates graph theory and a multi-criteria decision aid technique, presenting a different process for doing the investigation of criminal networks. In the criminal subject, privacy concerns limit identification. For this reason, the database composed of 110 actors, involving criminals and peripheral characters to the network, was identified by numbers, without names and penalties. The discrimination of critical actors in criminal networks can help law enforcement officers to conduct a more detailed investigation for their disruption. Communication between drug traffickers was transformed into different centrality indices for each actor in their social network. Centralities and actors compose a decision matrix, analyzed by the Composition of Probabilistic Preferences to identify the most relevant actors in the criminal network. Results indicated that the five actors highlighted in the real investigation have a clear distinction of importance in the network, which in a way have been ratified.

摘要 本研究将图论与多准则决策辅助技术相结合，提出了一种用于犯罪网络调查的创新流程。在犯罪研究领域，隐私保护顾虑限制了涉案主体的身份识别工作。为此，本次研究所使用的数据库包含110名主体，涵盖犯罪人员与该网络的外围角色，所有主体均以编号标识，未披露真实姓名与刑罚信息。对犯罪网络中的关键主体进行甄别，可协助执法人员开展更细致的调查以瓦解该网络。贩毒分子之间的通信记录被转化为该社交网络中各主体的多项中心性指标。以各中心性指标与主体信息构建决策矩阵，并通过概率偏好合成法（Composition of Probabilistic Preferences）对其进行分析，以此识别犯罪网络中的核心主体。研究结果表明，真实调查中重点关注的五名主体在网络重要性层面存在显著区分，该结论已得到一定程度的验证。