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Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2.The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs).

You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs.

Spektral implements some of the most popular layers for graph deep learning, including:

and many others (see convolutional layers).

You can also find pooling layers, including:

Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects.

See how to get started with Spektral and have a look at the examples for some templates.

The source code of the project is available on Github.Read the documentation here.

If you want to cite Spektral in your work, refer to our paper:

Graph Neural Networks in TensorFlow and Keras with SpektralDaniele Grattarola and Cesare Alippi

Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. Other Linux distros should work as well.

The simplest way to install Spektral is from PyPi:

To install Spektral from source, run this in a terminal:

To install Spektral on Google Colab:

The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements.

If you have already used Spektral in your projects, the only major change that you need to be aware of is the new datasets API.

This is a summary of the new features and changes:

Spektral is an open-source project available on Github, and contributions of all types are welcome. Feel free to open a pull request if you have something interesting that you want to add to the framework.

The contribution guidelines are available here and a list of feature requests is available here.

See original here:
graphneural.network - Spektral