Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. They have been successfully applied to a myriad of domains including chemistry, physics, social sciences, knowledge graphs, recommendation, and neuroscience. As the field grows, it becomes critical to identify the architectures and key mechanisms which generalize across graphs sizes, enabling us to tackle larger, more complex datasets and domains. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new GNNs and compare models in the absence of a standardized benchmark with consistent experimental settings and large datasets. In this paper, we propose a reproducible GNN benchmarking framework, with the facility for researchers to add new datasets and models conveniently. We apply this benchmarking framework to novel medium-scale graph datasets from mathematical modeling, computer vision, chemistry and combinatorial problems to establish key operations when designing effective GNNs. Precisely, graph convolutions, anisotropic diffusion, residual connections and normalization layers are universal building blocks for developing robust and scalable GNNs.

本文介绍了Graph Neural Networks (GNNs) 领域关于基准测试框架的应用，通过该框架，可对模型进行比较，并且探索新的GNN设计和洞见，其中，图形位置编码(PE)是该基准测试框架中引入的重要设计之一。

图神经网络基准测试