  We propose a simple discrete time semi-supervised graph embedding approach to
link prediction in dynamic networks. The learned embedding reflects information
from both the temporal and cross-sectional network structures, which is
performed by defining the loss function as a weighted sum of the supervised
loss from past dynamics and the unsupervised loss of predicting the
neighborhood context in the current network. Our model is also capable of
learning different embeddings for both formation and dissolution dynamics.
These key aspects contributes to the predictive performance of our model and we
provide experiments with three real--world dynamic networks showing that our
method is comparable to state of the art methods in link formation prediction
and outperforms state of the art baseline methods in link dissolution
prediction.
