  Event detection is a critical task for timely decision-making in graph
analytics applications. Despite the recent progress towards deep learning on
graphs, event detection on dynamic graphs presents particular challenges to
existing architectures. Real-life events are often associated with sudden
deviations of the normal behavior of the graph. However, existing approaches
for dynamic node embedding are unable to capture the graph-level dynamics
related to events. In this paper, we propose DyGED, a simple yet novel deep
learning model for event detection on dynamic graphs. DyGED learns correlations
between the graph macro dynamics -- i.e. a sequence of graph-level
representations -- and labeled events. Moreover, our approach combines
structural and temporal self-attention mechanisms to account for
application-specific node and time importances effectively. Our experimental
evaluation, using a representative set of datasets, demonstrates that DyGED
outperforms competing solutions in terms of event detection accuracy by up to
8.5% while being more scalable than the top alternatives. We also present case
studies illustrating key features of our model.
