In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-friendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral's features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression.

中文翻译：

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TensorFlow 和 Keras 中使用 Spektral 的图神经网络 [应用笔记]

在本文中，我们介绍了 Spektral，这是一个开源 Python 库，用于使用 TensorFlow 和 Keras 应用程序编程接口构建图神经网络。Spektral 实现了大量的图深度学习方法，包括消息传递和池化运算符，以及用于处理图和加载流行基准数据集的实用程序。这个库的目的是为创建图神经网络提供基本的构建块，重点是 Keras 所基于的用户友好性和快速原型设计的指导原则。因此，Spektral 适合绝对的初学者和专业的深度学习从业者。在这项工作中，我们概述了 Spektral 的功能，并报告了该库在节点分类场景中实现的方法的性能，