论文标题
在Tensorflow和Keras中图形神经网络与Spektral
Graph Neural Networks in TensorFlow and Keras with Spektral
论文作者
论文摘要
在本文中，我们介绍了一个开源Python库Spektral，用于构建具有Tensorflow和Keras应用程序编程接口的图形神经网络。 Spektral实现了一系列用于图形深度学习的方法，包括消息传动和汇总操作员，以及处理图和加载流行基准数据集的实用程序。该库的目的是提供创建图形神经网络的基本构建块，重点介绍用户友好性的指导原则以及Keras所基于的快速原型。因此，Spektral适合绝对的初学者和专家深度学习从业者。在这项工作中，我们概述了Spektral的功能，并报告了库在节点分类，图形分类和图形回归方案中实现的方法的性能。
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.
