A network of sensors deployed in urban environments continuously monitor for the presence of radioactive isotopes whether routine (i.e., medical procedures) or nefarious (i.e., nuclear proliferation). Unattended radiological sensor networks must take advantage of contextual data (open-source and historical sensor signals) to anticipate background isotope signatures across locations and sensors to mitigate nuance alarms. In our approach, we develop novel graph transformer networks to predict radiological sensor and isotope alerts with signals extracted from historical time series and context from nearby radiation sources. Our Dynamic Graph Transformers (DGT) models exceed the basic capability of Graph Neural Networks to analyze patterns in graph structure over time and predict links between nodes (i.e., sensors, hospitals, and construction sites), by learning from dynamic, time-dependent relationships. We extend the pre-existing state-of-the-art dynamic graph models, TGN and RENet, by incorporating Transformers for radiological sensor signal modeling and develop three model architectures. First, DGT-Continuous learns complex relationships between nodes from a sequence of time-stamped edges, and outputs the predicted probability of future edges between nodes. DGT-Discrete learns from a series of graph snapshots representing the relationship between nodes in the previous 24 hours, and predicts the next graph snapshot for the next interval. We have two variations of this model: DGT-D/G incorporates global context and DGT-D/GL incorporates local and global context. We pretrain the DGT models to use in two downstream tasks; (1) predicting the total number of alerts (regression) and (2) forecasting if an alert from each isotope and sensor will occur in the next time step. We leverage data collected from five sensors in Washington, DC between Oct. 2019 and Dec. 2020, and rely on traffic patterns between potential sources of radiation (hospitals and construction sites), to p