[论文解读] GraphSAINT: Graph Sampling Based Inductive Learning Method

Hanqing Zeng, Hongkuan Zhou+3 more|arXiv (Cornell University)|Jul 10, 2019

Advanced Graph Neural Networks参考文献 33被引用 336

一句话总结

GraphSAINT 提出了一种用于对大规模图进行归纳学习的图采样框架，通过在采样子图上进行训练。

ABSTRACT

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).

研究动机与目标

• 使用子图采样实现对大规模图的可扩展归纳学习。
• 在采样过程中通过节点预算和边预算来控制计算资源。
• 利用根节点和随机游走来构建用于训练的有信息量的子图。

提出的方法

• 指定采样参数：节点预算 n、边预算 m、根节点数量 r，以及随机游走长度 h。
• 从原始图 G(V,E) 中采样子图 Gs(Vs, Es) 以创建训练数据。
• 将采样的子图转换为适合图神经网络训练的训练单元。
• 使用基于根节点或基于游走的策略来驱动多样且具代表性的子图采样。

实验结果

研究问题

• RQ1 子图采样如何影响归纳式图学习的准确性和泛化能力？
• RQ2 节点预算与边预算与模型性能之间存在哪些权衡？
• RQ3 基于随机游走的采样是否能产生对训练有信息量的子图？
• RQ4 采样策略如何影响对极大图的可扩展性？

主要发现

• 在所提供的节选中不可用；所提供的文本不包含实验结果或定量发现。