Rethinking The Memory Staleness Problem In Dynamics GNN
This addresses a known bottleneck in dynamic GNNs for applications like social network analysis, but appears incremental.
The paper tackles the memory staleness problem in dynamic graph neural networks by proposing an updated embedding module that incorporates the most similar node along with neighbors' memories, achieving results comparable to TGN with slight improvements.
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.