  In the last 20 years, terrorism has led to hundreds of thousands of deaths
and massive economic, political, and humanitarian crises in several regions of
the world. Using real-world data on attacks occurred in Afghanistan and Iraq
from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning
to forecast future terrorist targets. Focusing on three event dimensions, i.e.,
employed weapons, deployed tactics and chosen targets, meta-graphs map the
connections among temporally close attacks, capturing their operational
similarities and dependencies. From these temporal meta-graphs, we derive
2-day-based time series that measure the centrality of each feature within each
dimension over time. Formulating the problem in the context of the strategic
behavior of terrorist actors, these multivariate temporal sequences are then
utilized to learn what target types are at the highest risk of being chosen.
The paper makes two contributions. First, it demonstrates that engineering the
feature space via temporal meta-graphs produces richer knowledge than shallow
time-series that only rely on frequency of feature occurrences. Second, the
performed experiments reveal that bi-directional LSTM networks achieve superior
forecasting performance compared to other algorithms, calling for future
research aiming at fully discovering the potential of artificial intelligence
to counter terrorist violence.
