Complex networks can shed light on the hidden patterns that connect terrorist actors

网络科学经常被证明是研究和分析社会现象的有效框架。它的优势也可以在恐怖主义研究的异质领域中得到利用。在一项发表的研究中应用网络科学,Gian Maria Campedelli,Iain Cruickshank和Kathleen M. Carley教授在场an algorithmic framework for identifying latent communities of terrorist groups,基于对其运营特征的分析。

When discussing about terrorist groups, the public debate is generally centered around their ideology and motives. The main distinctions that are made to classify them indeed regards their motivations and background. Jihadist groups, far-right organizations, anarchists or independentist factions are just some of the distinct labels that are used to discriminate these actors.

而意识形态certainly an important component in the study of terrorism, it may not be the best element to use in order to comprehensively investigate terrorism. What if, instead, we use historical information on the events they are responsible for? Can operational and behavioral data help in better understanding the phenomenon? We have attempted to respond these questions提出一种新方法that relies on the science of complex networks.

从复杂的网络角度查看恐怖主义数据

Networks have been extensively used in terrorism research mostly to map the physical relations between individuals belonging to the same organization: edges represented, for instance, the presence of communication flows between two actors. Graphs have also been used to assess the strength and resilience of terrorist groups against potential intelligence operations.

事实证明,这些群体的拓扑和结构是灵活的,并且通常在反击后恢复。尽管越来越丰富的文献应用了这种方法来展现群体的内部动力学,但科学家尚未测试网络在代表更复杂关系的能力,而这种关系可能不会立即可见或可理解。

我们已经检索了全球恐怖主义数据库中存在的恐怖行为者的所有数据,该数据绘制了1997年至2016年至少一次攻击。使用有关其攻击的信息,我们构建了一个多目标网络,该网络映射了给定的存在的存在演员和某个地区,战术,武器和目标。网络中边缘的存在表达了该演员实际攻击区域或使用武器历史上的频率。

After creating such abstract representation, we then used Von Neumann entropy as a weighting factor to capture the inherent complexity of each mode of the graph. We have further used Gower’s coefficient to map the pairwise similarity between groups as a product of their operational characteristics. Having obtained a matrix of similarities, we then applied the Louvain algorithm to detect latent clusters of similar terrorist groups based on those coefficients.

仅意识形态是研究恐怖主义的薄弱标准

The analysis of the resulting clusters highlighted several interesting patterns that corroborate our initial hypothesis: ideology alone is an extremely weak criterion for studying terrorism. In fact, clusters had a heterogeneous nature in terms of ideology: very few of them only contained groups associated to a unique ideological label.

From this heterogeneity emerged that, instead, groups belonging to certain ideologies are operationally very similar to others that may fight for the opposite motivations. This is the case, for instance, of far-right and far-left terrorist groups: the correlation is positive and relatively strong between these two ideologies with regards to cluster assignment. A similar pattern is found when comparing ethno-nationalist and far-right groups.

Our exploratory study is one of the very first attempts to use complex networks to characterize hidden non-temporal and non-spatial relations and use them as mathematical concepts for investigating the nature of terrorism. Our intuition is that networks are still overly underestimated tools for understanding complex criminal phenomena and we plan to design our future research at to fill this gap in the literature.

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