Belfer Center for Science and International Affairs, Harvard University, Cambridge, MA, United States of America.
Department of Criminology and Criminal Justice, University of Maryland, College Park, Maryland, United States of America.
PLoS One. 2024 Jun 5;19(6):e0298273. doi: 10.1371/journal.pone.0298273. eCollection 2024.
Prior research suggests that members of terrorist groups prioritize forming network ties based on trust to improve their organizational and operational security. The homophily principle, which postulates that individuals tend to form relationships based on shared characteristics, can be a key mechanism through which people identify trustworthy associates. Next to homophily, the mechanism of establishing interconnected relationships through transitivity is also well-known to serve this purpose and shape community structures in social networks. We analyze the community structures of the Islamist co-offending network in the United States, which is highly violent, to assess whether homophily and transitivity determine which extremists form co-offending ties. We rely on a new database on the individual attributes and the co-offending relationships of 494 Islamist offenders radicalized in the United States between 1993 and 2020. Using community detection algorithms, we show that the US Islamist co-offending network is highly clustered, modular, and includes many small but only a few large communities. Furthermore, results from exponential random graph modeling show that transitive relationships as well as spatial proximity, ideological affiliation, and shared socio-cultural characteristics drive co-offending among US Islamist extremists. Overall, these findings demonstrate that the processes of homophily and transitivity shape violent social networks.
先前的研究表明,恐怖组织成员优先基于信任建立网络联系,以提高其组织和运营安全性。同质性原则假定个人倾向于基于共同特征建立关系,这可以成为人们识别可信赖伙伴的关键机制。除了同质性之外,通过传递性建立互联关系的机制也众所周知,它可以服务于这一目的,并塑造社交网络中的社区结构。我们分析了美国伊斯兰极端主义共同犯罪网络的社区结构,该网络具有高度暴力性,以评估同质性和传递性是否决定了哪些极端分子形成共同犯罪关系。我们依赖于一个关于美国在 1993 年至 2020 年期间激进化的 494 名伊斯兰极端分子的个体属性和共同犯罪关系的新数据库。使用社区检测算法,我们表明美国伊斯兰极端分子共同犯罪网络高度聚类、模块化,并且包含许多小的但只有少数大的社区。此外,指数随机图模型的结果表明,传递关系以及空间接近度、意识形态关联和共同的社会文化特征推动了美国伊斯兰极端分子之间的共同犯罪。总的来说,这些发现表明同质性和传递性的过程塑造了暴力社交网络。