This work analyzes Graph Neural Networks, a generalization of Fully-Connected
Deep Neural Nets on Graph structured data, when their width, that is the number
of nodes in each fullyconnected layer is increasing to infinity. Infinite Width
Neural Networks are connecting Deep Learning to Gaussian Processes and Kernels,
both Machine Learning Frameworks with long traditions and extensive theoretical
foundations. Gaussian Processes and Kernels have much less hyperparameters then
Neural Networks and can be used for uncertainty estimation, making them more
user friendly for applications. This works extends the increasing amount of
research connecting Gaussian Processes and Kernels to Neural Networks. The
Kernel and Gaussian Process closed forms are derived for a variety of
architectures, namely the standard Graph Neural Network, the Graph Neural
Network with Skip-Concatenate Connections and the Graph Attention Neural
Network. All architectures are evaluated on a variety of datasets on the task
of transductive Node Regression and Classification. Additionally, a Spectral
Sparsification method known as Effective Resistance is used to improve runtime
and memory requirements. Extending the setting to inductive graph learning
tasks (Graph Regression/ Classification) is straightforward and is briefly
discussed in 3.5.

增加节点宽度对于图神经网络及其各种体系结构（如带有跳连接的图神经网络和图注意网络）的研究，并推导了相关的核和高斯过程闭合形式，应用于节点回归和分类任务，并使用谱稀疏化方法改善运行时间和内存需求。此外，研究还简要探讨了对于归纳图学习任务（如图回归 / 分类）的扩展。