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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 isupdated 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 asocial account. To overcome the memory staleness problem aggregate informationfrom the nodes neighbors memory in addition to the nodes memory. Inspired bythat, we design an updated embedding module that inserts the most similar nodein addition to the nodes neighbors. Our method achieved similar results to theTGN, with a slight improvement. This could indicate a potential improvementafter fine-tuning our hyper-parameters, especially the time threshold, andusing a learnable similarity metric.

Rethinking The Memory Staleness Problem In Dynamics GNN