Graph representation learning has drawn increasing attention in recent years, especially for learning the low dimensional embedding at both node and graph level for classification and recommendations tasks. To enable learning the representation on the large-scale graph data in the real world, numerous research has focused on developing different sampling strategies to facilitate the training process. Herein, we propose an adaptive Graph Policy-driven Sampling model (GPS), where the influence of each node in the local neighborhood is realized through the adaptive correlation calculation. Specifically, the selections of the neighbors are guided by an adaptive policy algorithm, contributing directly to the message aggregation, node embedding updating, and graph level readout steps. We then conduct comprehensive experiments against baseline methods on graph classification tasks from various perspectives. Our proposed model outperforms the existing ones by 3%-8% on several vital benchmarks, achieving state-of-the-art performance in real-world datasets.

翻译：图表代表学习近年来引起越来越多的注意,特别是学习在节点和图表层次上低维嵌入的分类和建议任务。为了能够了解在现实世界中大型图表数据中的代表性,许多研究侧重于制定不同的抽样战略以促进培训进程。在这里,我们提议了一个适应性图表政策驱动抽样模型(GPS),通过适应性相关计算实现每个节点对当地社区的影响。具体地说,邻居的选择以适应性政策算法为指导,直接促进信息集成、节点嵌入更新和图形水平读取步骤。我们随后对图表分类工作的各种基准方法进行了全面实验。我们提议的模型在几个关键基准上比现有的模型高出3%-8%,在现实世界数据集中实现了最先进的表现。