Effective detection of organizations is essential for fighting crime and maintaining public safety, especially considering the limited human resources and tools to deal with each group that exhibits co-movement patterns. This paper focuses on solving the Network Structure Inference (NSI) challenge. Thus, we introduce two new approaches to detect network structure inferences based on agent trajectories. The first approach is based on the evaluation of graph entropy, while the second considers the quality of clustering indices. To evaluate the effectiveness of the new approaches, we conducted experiments using four scenario simulations based on the animal kingdom, available on the NetLogo platform: Ants, Wolf Sheep Predation, Flocking, and Ant Adaptation. Furthermore, we compare the results obtained with those of an approach previously proposed in the literature, applying all methods to simulations of the NetLogo platform. The results demonstrate that our new detection approaches can more clearly identify the inferences of organizations or networks in the simulated scenarios.

翻译：有效检测犯罪组织对于打击犯罪和维护公共安全至关重要，尤其在处理表现出协同运动模式的群体时，人力与工具有限的现状更凸显其必要性。本文聚焦于解决网络结构推断（NSI）难题，为此提出了两种基于智能体轨迹检测网络结构推断的新方法。第一种方法基于图熵评估，第二种则考虑聚类指标的质量。为评估新方法的有效性，我们基于动物王国在NetLogo平台上开展了四类场景模拟实验：蚂蚁模型、狼羊捕食模型、鸟群模型和蚂蚁适应模型。此外，我们将实验结果与现有文献方法的结果进行了对比，所有方法均应用于NetLogo平台的仿真场景。结果表明，我们的新检测方法能更清晰地识别模拟场景中的组织或网络推断特征。