TY - JOUR
T1 - The Geometrical Shapes of Violence: Predicting and Explaining Terrorist Operations Through Graph Embeddings
AU - Campedelli, Gian Maria
AU - Layne, Janet
AU - Herzoff, Jack
AU - Serra, Edoardo
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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.
AB - 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.
KW - complex networks
KW - conflict
KW - graph learning
KW - neural networks
KW - political violence
KW - security
UR - https://scholarworks.boisestate.edu/cs_facpubs/354
UR - https://doi.org/10.1093/comnet/cnac008
M3 - Article
JO - Computer Science Faculty Publications and Presentations
JF - Computer Science Faculty Publications and Presentations
ER -