Graphs with Python: Overview and Best Libraries

Graph analysis, interactive visualizations, and graph machine learning

A graph is a relatively old mathematical data entity that is a set of connected elements. Since the graph is a very flexible structure and allows you to store information in a form familiar and convenient to humans, graphs have always been used in computer science and technology. With the rise of machine learning and deep learning, graphs have gained even more popularity by creating the field of graph machine learning.

In this post, I would like to share with you the most useful Python libraries I’ve used for graph/network analysis, visualization, and machine learning. Today, we will review:

• NetworkX for general graph analysis;
• PyVis for interactive graph visualizations right in your browser;
• PyG and DGL for solving various graph machine learning tasks.

Before that, let me tell you a few words about graph theory and graph machine learning and provide some learning resources that may be helpful to you. If you don’t know what graph or graph machine learning is, that is a great opportunity to lift the veil of secrecy!