The Geometrical Shapes of Violence: Predicting and Explaining Terrorist Operations Through Graph Embeddings
Behaviours across terrorist groups differ based on a variety of factors, such as groups’ resources or objectives. We here show that organizations can also be distinguished by network representations of their operations. We provide evidence in this direction in the frame of a computational methodology organized in two steps, exploiting data on attacks plotted by Al Shabaab, Boko Haram, the Islamic State and the Taliban in the 2013–2018 period. First, we present LabeledSparseStruct, a graph embedding approach, to predict the group associated with each operational meta-graph. Second, we introduce SparseStructExplanation, an algorithmic explainer based on LabeledSparseStruct, that disentangles characterizing features for each organization, enhancing interpretability at the dyadic level. We demonstrate that groups can be discriminated according to the structure and topology of their operational meta-graphs, and that each organization is characterized by the recurrence of specific dyadic interactions among event features.
Campedelli, Gian Maria; Layne, Janet; Herzoff, Jack; and Serra, Edoardo. (2022). "The Geometrical Shapes of Violence: Predicting and Explaining Terrorist Operations Through Graph Embeddings". Journal of Complex Networks, 10(2), cnac008. https://doi.org/10.1093/comnet/cnac008