论文标题
重新考虑动力学中的记忆陈旧问题
Rethinking The Memory Staleness Problem In Dynamics GNN
论文作者
论文摘要
由于长期没有事件，处理动态数据时，陈旧问题是一个众所周知的问题。由于仅当节点参与事件时才更新节点的内存，因此其内存变为陈旧。通常，它是指缺乏社会账户的时间停用等事件。为了克服内存的陈旧问题问题，除节点内存外，来自节点的汇总信息。受此启发的启发，我们设计了一个更新的嵌入模块，该模块除节点邻居外还插入最相似的节点。我们的方法获得了与TGN相似的结果，并有所改善。这可能表明在微调我们的超参数后，尤其是时间阈值并使用可学习的相似性度量后，可能会有所改善。
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.
