  Communities in social networks evolve over time as people enter and leave the
network and their activity behaviors shift. The task of predicting structural
changes in communities over time is known as community evolution prediction.
Existing work in this area has focused on the development of frameworks for
defining events while using traditional classification methods to perform the
actual prediction. We present a novel graph neural network for predicting
community evolution events from structural and temporal information. The model
(GNAN) includes a group-node attention component which enables support for
variable-sized inputs and learned representation of groups based on member and
neighbor node features. A comparative evaluation with standard baseline methods
is performed and we demonstrate that our model outperforms the baselines.
Additionally, we show the effects of network trends on model performance.
