This work is on an extrapolation problem which is to make predictions at unobserved times, different from interpolation work.
Proposes a novel neural architecture for modeling complex entity interaction sequences, which consists of a recurrent event encoder and a neighborhood aggregator.
Explores various neighborhood aggregators: a multi-relational graph aggregator demonstrates its effectiveness among them.