Graph Neural Networks in TensorFlow and Keras with Spektral
This provides a user-friendly tool for both beginners and experts to quickly prototype graph neural networks, but it is incremental as it builds on existing frameworks without introducing new methods.
The authors introduced Spektral, an open-source Python library for building graph neural networks using TensorFlow and Keras, and reported its performance in node classification, graph classification, and graph regression scenarios.
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.