Now You Can Build Graph Neural Networks With Spektral Based On Keras

Recently, a PhD researcher, Daniele Grattarola built a framework known as Spektral for mapping relational representation learning which is built in Python and is based on the Keras API. Spektral contains a comprehensive set of tools to build graph neural networks as well as implement some of the popular layers for graph Deep Learning. Spektral is built with semi-supervised deep learning methods for graph data, Graph Neural Network (GNN). According to this paper, the idea of GNN is simple: to encode structural information of the graph, each node v_i can be represented by a low-dimensional state vector s_i, 1 i N. The categorisation of deep learning methods on the graph is shown below The core GNN modules of Spektral are mainly based on Keras and it works with all the different backends offered by Keras in order to promptly start experimenting with the Relational Representation Learning (RRL) which consists of developing models that are able to deal with graphs natively without having to deal with the distracting low-level details. The accessory modules of Spectral are built in Numpy/Scipy and for which everything should work at the speed of almost-C-like and without compatibility issues.