  Temporal graph neural networks Tgnn have exhibited state-of-art performance
in future-link prediction tasks. Training of these TGNNs is enumerated by
uniform random sampling based unsupervised loss. During training, in the
context of a positive example, the loss is computed over uninformative
negatives, which introduces redundancy and sub-optimal performance. In this
paper, we propose modified unsupervised learning of Tgnn, by replacing the
uniform negative sampling with importance-based negative sampling. We
theoretically motivate and define the dynamically computed distribution for a
sampling of negative examples. Finally, using empirical evaluations over three
real-world datasets, we show that Tgnn trained using loss based on proposed
negative sampling provides consistent superior performance.
