  We present a new neighbor sampling method on temporal graphs. In a temporal
graph, predicting different nodes' time-varying properties can require the
receptive neighborhood of various temporal scales. In this work, we propose the
TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information
to provide an adaptive receptive neighborhood for every node at any time.
Learning how to sample neighbors is non-trivial, since the neighbor indices in
time order are discrete and not differentiable. To address this challenge, we
transform neighbor indices from discrete values to continuous ones by
interpolating the neighbors' messages. TNS can be flexibly incorporated into
popular temporal graph networks to improve their effectiveness without
increasing their time complexity. TNS can be trained in an end-to-end manner.
It needs no extra supervision and is automatically and implicitly guided to
sample the neighbors that are most beneficial for prediction. Empirical results
on multiple standard datasets show that TNS yields significant gains on edge
prediction and node classification.
