Graph Neural Networks (GNNs) have achieved great successes in many learning tasks performed on graph structures. Nonetheless, to propagate information GNNs rely on a message passing scheme which can become prohibitively expensive when working with industrial-scale graphs. Inspired by the PPRGo model, we propose the CorePPR model, a scalable solution that utilises a learnable convex combination of the approximate personalised PageRank and the CoreRank to diffuse multi-hop neighbourhood information in GNNs. Additionally, we incorporate a dynamic mechanism to select the most influential neighbours for a particular node which reduces training time while preserving the performance of the model. Overall, we demonstrate that CorePPR outperforms PPRGo, particularly on large graphs where selecting the most influential nodes is particularly relevant for scalability. Our code is publicly available at: https://github.com/arielramos97/CorePPR.
翻译:图像神经网络(Neural Networks) 在许多在图形结构上完成的学习任务中取得了巨大成功。 然而,为了传播信息,GNNS依靠的是信息传递计划,在与工业级图表合作时,这种计划会变得极其昂贵。在PPRGO模型的启发下,我们提出了CorePPR模型,这是一个可扩展的解决方案,它利用大约个人化的PageRank和CoreRank的可学习的组合,在图形结构中传播多点邻居信息。此外,我们引入了一个动态机制,为某个特定节点选择最有影响力的邻居,减少培训时间,同时保留模型的性能。总的来说,我们证明CorePPRP优于PGO,特别是在选择最有影响力的节点与可扩展性特别相关的大图上。我们的代码公布在https://github.com/arielramos97/CorePPRR。