GPS: A Policy-driven Sampling Approach for Graph Representation Learning. (arXiv:2112.14482v2 [cs.LG] UPDATED)

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 …

arxiv graph learning policy