Graph Neural Networks (GNN) Frameworks

- 01. Plotly's Python graphing library makes interactive, publication-quality graphs
- 02. igraph is on the Python Package Index with pre-compiled wheels for most Python distributions and platforms,
- 03. NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
- 04. PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
- 05. Deep Graph Library - Fast and memory-efficient message passing primitives for training Graph Neural Networks.
- 06.Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet.
- 07. Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2.
- 08. GeometricFlux package! GeometricFlux is a framework for geometric deep learning/machine learning. It provides classic graph neural network layers and some utility constructs.
- 09. Jraph is a lightweight library for working with graph neural networks in jax. It provides a data structure for graphs, a set of utilites for working with graphs, and a ‘zoo’ of forkable graph neural network models.
- 10. ptgnn: A PyTorch GNN Library containing pyTorch code for creating graph neural network (GNN) models. The library provides some sample implementations.