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A guide on who should use it, when to use it, how to use it, and why I was wrong before…
In this article, I will introduce to you a Python package I stumbled upon that is, in my humble opinion, the BEST tool I have seen so far for visualising network graphs.
Readers who are data scientists in need of a compact yet powerful visualisation package for quick prototyping, exploratory data analysis or debugging their network models are best suited for the contents below.
The package that we will be inspecting is called:
I personally use graph neural networks a lot in my day-to-day job, and quite frankly, I am annoyed that I didn’t know about this package earlier as it would have saved me a lot of time and energy trying to work around the shortcomings of the packages (
pyvis) that I wrote about here:
What makes a network visualisation package the best?
A visualisation package needs to:
- Create a fully interactive visualisation, where I can click on nodes and edges and view its attributes, plus drag and drop them.
- Convenient to implement — doesn’t require too much code (like Dash), but powerful and flexible enough for most use cases.
- Moderately good scalability to the number of nodes and edges — we’re not making something for prod, but we need it to handle hundreds of nodes at least.
- Compatible with commonly used network packages in Python such as
What will we be testing the package…
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