Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse
array of applications. However, the sheer size of large-scale graphs presents a
significant challenge to real-time inference with GNNs. Although existing
Scalable GNNs leverage linear propagation to preprocess the features and
accelerate the training and inference procedure, these methods still suffer
from scalability issues when making inferences on unseen nodes, as the feature
preprocessing requires the graph to be known and fixed. To further accelerate
Scalable GNNs inference in this inductive setting, we propose an online
propagation framework and two novel node-adaptive propagation methods that can
customize the optimal propagation depth for each node based on its topological
information and thereby avoid redundant feature propagation. The trade-off
between accuracy and latency can be flexibly managed through simple
hyper-parameters to accommodate various latency constraints. Moreover, to
compensate for the inference accuracy loss caused by the potential early
termination of propagation, we further propose Inception Distillation to
exploit the multi-scale receptive field information within graphs. The rigorous
and comprehensive experimental study on public datasets with varying scales and
characteristics demonstrates that the proposed inference acceleration framework
outperforms existing state-of-the-art graph inference acceleration methods in
terms of accuracy and efficiency. Particularly, the superiority of our approach
is notable on datasets with larger scales, yielding a 75x inference speedup on
the largest Ogbn-products dataset.

大规模图的实时推断对于图神经网络 (GNNs) 提出了一项重大挑战，但我们提出了一种在线传播框架和两种新的节点自适应传播方法来加速可扩展 GNNs 的推断过程，并通过简单的超参数灵活管理准确性和时延之间的平衡。此外，我们进一步提出了 Inception Distillation 来弥补推断准确性损失，实验结果显示我们的方法在精确性和效率上优于现有的图推断加速方法。