  Graph neural networks (GNNs) have demonstrated strong performance on a wide
variety of tasks due to their ability to model non-uniform structured data.
Despite their promise, there exists little research exploring methods to make
them more efficient at inference time. In this work, we explore the viability
of training quantized GNNs, enabling the usage of low precision integer
arithmetic during inference. We identify the sources of error that uniquely
arise when attempting to quantize GNNs, and propose an architecturally-agnostic
method, Degree-Quant, to improve performance over existing quantization-aware
training baselines commonly used on other architectures, such as CNNs. We
validate our method on six datasets and show, unlike previous attempts, that
models generalize to unseen graphs. Models trained with Degree-Quant for INT8
quantization perform as well as FP32 models in most cases; for INT4 models, we
obtain up to 26% gains over the baselines. Our work enables up to 4.7x speedups
on CPU when using INT8 arithmetic.
