Graph Neural Networks in TensorFlow and Keras with Spektral
- URL: http://arxiv.org/abs/2006.12138v1
- Date: Mon, 22 Jun 2020 10:56:22 GMT
- Title: Graph Neural Networks in TensorFlow and Keras with Spektral
- Authors: Daniele Grattarola, Cesare Alippi
- Abstract summary: Spektral is an open-source Python library for building graph neural networks.
It implements a large set of methods for deep learning on graphs, including message-passing and pooling operators.
It is suitable for absolute beginners and expert deep learning practitioners alike.
- Score: 18.493394650508044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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