计算机应用与软件2026，Vol.43Issue(4)：110-115,152,7.DOI:10.3969/j.issn.1000-386x.2026.04.015
面向层次采样的大规模动态图表示学习策略
LARGE-SCALE DYNAMIC GRAPHS REPRESENT LEARNING STRATEGIES WITH LAYER-WISE SAMPLING
摘要
Abstract
The original full-batch training for dynamic graph neural networks requires calculating the representation of all the nodes in the graph,which is difficult to handle large-scale data due to the limited memory capacity and computing power of current hardware devices.This paper proposes a representation learning strategies of dynamic graph neural network based on layer dependent importance sampling.Some nodes in the dynamic graph were selected as initial nodes,and these initial nodes sampled neighboring nodes layer by layer based on importance probability.The sampled nodes contained the spatiotemporal neighborhood information of the dynamic graph,which could guarantee the model training effect.Experiments were conducted on four publicly available datasets using three different models.The results show that using this strategy for training can reduce the memory overhead of the hardware device and improve the training effect of the model to a certain extent.关键词
图神经网络/层次采样/动态图/大规模数据Key words
Graph neural networks/Layer-wise sampling/Dynamic graph/Large-scale dataset分类
信息技术与安全科学引用本文复制引用
王健,裴中辉..面向层次采样的大规模动态图表示学习策略[J].计算机应用与软件,2026,43(4):110-115,152,7.基金项目
国家自然科学基金面上项目(61572229). （61572229）
