spektral

Introduction to Spektral

Spektral is a sophisticated yet user-friendly Python library designed for graph deep learning, built upon the Keras API and TensorFlow 2. Its main objective is to offer a straightforward and adaptable framework for constructing graph neural networks (GNNs). This tool is versatile and can be employed in various tasks such as classifying social network users, predicting molecular properties, generating new graphs using GANs, clustering nodes, and link prediction, among others where data is represented by graphs.

Key Features

Graph Layers

Spektral implements numerous popular graph deep learning layers, including but not limited to:

• Graph Convolutional Networks (GCN): A fundamental layer for processing graph-structured data.
• Chebyshev Convolutions: Efficient approximations of spectral graph convolutions.
• GraphSAGE: An approach to generate node embeddings for large graphs.
• ARMA Convolutions: Provides a method for stable, robust graph signal processing.
• Edge-Conditioned Convolutions (ECC): Incorporates edge attributes into graph convolution operations.
• Graph Attention Networks (GAT): Allows nodes to attend over their neighborhoods’ features, with varying attention weights.
• Approximated Personalized Propagation of Neural Predictions (APPNP): Combines personalized PageRank with neural prediction.
• Graph Isomorphism Networks (GIN): Achieves a highly discriminative power of graph structures.
• Diffusional Convolutions: Utilizes diffusion-based methods for processing graphs.

Pooling Layers

Pooling is critical for dimensionality reduction and abstract representation learning in graphs. Spektral provides various pooling layers such as:

• MinCut pooling
• DiffPool
• Top-K pooling
• Self-Attention Graph (SAG) pooling
• Global Pooling Variants
• Global Gated Attention Pooling
• SortPool

Utilities and Support

Spektral equips users with utilities for representing, manipulating, and transforming graphs to suit their graph deep learning needs. It includes plenty of examples to help users get started quickly.

Getting Started

You can explore how to get started with Spektral on their official page and browse comprehensive examples to guide through various use-case templates. The source code is hosted on GitHub, and detailed documentation can be found here.

Installation

Spektral is developed to be compatible with Python 3.6 and above and is tested across the latest versions of Ubuntu, MacOS, and Windows. Installation can be easily achieved via:

pip install spektral

Or to install from source:

git clone https://github.com/danielegrattarola/spektral.git
cd spektral
python setup.py install  # Or 'pip install .'

In addition, for usage in Google Colab, simply run:

! pip install spektral

New Features in Spektral 1.0

The 1.0 release marks a significant evolution for Spektral with a host of new features and enhancements:

• Introduction of new Graph and Dataset containers to streamline data handling.
• A Loader class that simplifies graph batch creation.
• A transforms module for applying common graph operations.
• GeneralConv and GeneralGNN classes for building comprehensive models using cutting-edge research.
• Addition of new datasets like QM7 and ModelNet10/40, as well as a new OGB dataset wrapper.
• A major cleanup of the library's structure and dependencies alongside a collection of new examples and tutorials.

Contributing to Spektral

As an open-source initiative, Spektral warmly welcomes contributions and collaboration. Its repository on GitHub invites users to participate by submitting pull requests for interesting enhancements. Contribution guidelines and feature requests are available to facilitate engagement and improvement of the project.

By offering a flexible and easy-to-use library, Spektral continues to democratize access to advanced graph deep learning techniques, empowering users to tackle complex graph-based problems with ease.