  The staleness problem is a well-known problem when working with dynamic data,
due to the absence of events for a long time. Since the memory of the node is
updated only when the node is involved in an event, its memory becomes stale.
Usually, it refers to a lack of events such as a temporal deactivation of a
social account. To overcome the memory staleness problem aggregate information
from the nodes neighbors memory in addition to the nodes memory. Inspired by
that, we design an updated embedding module that inserts the most similar node
in addition to the nodes neighbors. Our method achieved similar results to the
TGN, with a slight improvement. This could indicate a potential improvement
after fine-tuning our hyper-parameters, especially the time threshold, and
using a learnable similarity metric.
