Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a complex networks approach. This approach will allow finding latent clusters of similar terror groups using information on their operational characteristics. Specifically, using open access data of terrorist attacks occurred worldwide from 1997 to 2016, we build a multi-partite network that includes terrorist groups and related information on tactics, weapons, targets, active regions. We propose a novel algorithm for cluster formation that expands our earlier work that solely used Gower's coefficient of similarity via the application of Von Neumann entropy for mode-weighting. This novel approach is compared with our previous Gower-based method and a heuristic clustering technique that only focuses on groups' ideologies. The comparative analysis demonstrates that the entropy-based approach tends to reliably reflect the structure of the data that naturally emerges from the baseline Gower-based method. Additionally, it provides interesting results in terms of behavioral and ideological characteristics of terrorist groups. We furthermore show that the ideology-based procedure tends to distort or hide existing patterns. Among the main statistical results, our work reveals that groups belonging to opposite ideologies can share very common behaviors and that Islamist/jihadist groups hold peculiar behavioral characteristics with respect to the others. Limitations and potential work directions are also discussed, introducing the idea of a dynamic entropy-based framework.

中文翻译：

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一种寻找潜在恐怖组织集群的复杂网络方法

鉴于参与恐怖主义行动的参与者和团体的极端异质性，调查和评估他们的特征对于提取相关信息和增强对其行为的了解非常重要。目前的工作将寻求通过复杂的网络方法来实现这一目标。这种方法将允许使用有关其操作特征的信息来发现类似恐怖组织的潜在集群。具体而言，我们利用 1997 年至 2016 年全球发生的恐怖袭击的开放获取数据，构建了一个多方网络，其中包括恐怖组织以及相关的战术、武器、目标、活动区域信息。我们提出了一种新的集群形成算法，扩展了我们早期仅使用 Gower 的工作 s 相似系数通过应用冯诺依曼熵进行模式加权。这种新颖的方法与我们之前基于 Gower 的方法和仅关注群体意识形态的启发式聚类技术进行了比较。比较分析表明，基于熵的方法倾向于可靠地反映从基于 Gower 的基线方法自然出现的数据结构。此外，它在恐怖组织的行为和意识形态特征方面提供了有趣的结果。我们进一步表明，基于意识形态的程序往往会扭曲或隐藏现有的模式。在主要统计结果中，我们的工作表明，属于相反意识形态的群体可以共享非常常见的行为，并且伊斯兰/圣战组织相对于其他群体具有独特的行为特征。还讨论了局限性和潜在的工作方向，介绍了基于动态熵的框架的想法。