In this article, I will introduce you to a Python package that I stumbled upon and which is, IMHO, the BETTER tool I have seen so far to visualize network graphs.
Readers who are data scientists and need a compact yet powerful visualization package for rapid prototyping, exploratory data analysis, or debugging their network models are best suited for the following content.
The package we will inspect is called: gravis
I personally use graph neural networks a lot in my daily work and frankly it bothers me that I didn’t know about this package sooner as it would have saved me a lot of time and energy trying to figure it out. the deficiencies of the packages (ipysigma and pyvis) which I wrote about here:
What makes a best network display package?
A display package needs:
- Create a fully interactive visualization, where I can click on nodes and edges and see their attributes, as well as drag and drop them.
- Convenient to deploy: Doesn’t require too much code (like Dash), but is powerful and flexible enough for most use cases.
- Moderately good scalability for the number of nodes and edges – we’re not building something for production, but we need it to handle at least hundreds of nodes.
- Supports commonly used network packages in Python, such as
networkx.
