# Rethinking The Memory Staleness Problem In Dynamics GNN

arXiv: https://arxiv.org/abs/2209.02462 · HTML: https://mlsanity.com/paper/2209.02462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Representation Learning for Dynamic Graphs: A Survey](https://mlsanity.com/paper/1905.11485/) (2019)
- [Dynamic Graph Representation Learning via Self-Attention Networks](https://mlsanity.com/paper/1812.09430/) (2018)
- [3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting](https://mlsanity.com/paper/1903.00919/) (2019)

## Abstract (arXiv)

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.