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Export the graph to GML or to GraphML or to GraphViz DOT or to some other Graph format. BTW I recommend 3D graph visualization over 2D when possible, that is when you're exploring interactively as opposed to printing figures. The Graphia tool is the only FOSS tool for this purpose that I know of:
https://graphia.app
https://github.com/graphia-app/graphia
We make it pretty easy to go from networkx or any other pydata (DF, csv, parquet, ...) to interactive GPU viz w all sorts of analytics built in: https://github.com/graphistry/pygraphistry#explore-any-data-...
If you need more speed (but less features), check out networkit - https://networkit.github.io/
See also https://github.com/Qiskit/rustworkx – a general purpose graph library for Python written in Rust to take advantage of the performance and safety that Rust provides.
> Rustworkx was originally called retworkx and was created initially to be a replacement for qiskit's previous (and current) NetworkX usage (hence the original name). The project was originally started to build a faster directed graph to use as the underlying data structure for the DAG at the center of qiskit-terra's transpiler. However, since it's initial introduction the project has grown substantially and now covers all applications that need to work with graphs which includes Qiskit.
You may like my Netgraph library [1], which is a Python library that aims to complement networkx, igraph, and graph-tool with publication-quality visualisations.
Netgraph implements numerous node layout algorithms and several edge routing routines. Uniquely among Python alternatives, it handles networks with multiple components gracefully (which otherwise break most node layout routines), and it post-processes the output of the node layout and edge routing algorithms with several heuristics to increase the interpretability of the visualisation (reduction of overlaps between nodes, edges, and labels; edge crossing minimisation and edge unbundling where applicable). The highly customisable plots are created using Matplotlib, and the resulting Matplotlib objects are exposed in an easily queryable format such that they can be further manipulated and/or animated using standard Matplotlib syntax. Finally, Netgraph also supports interactive changes: with the InteractiveGraph class, nodes and edges can be positioned using the mouse, and the EditableGraph class additionally supports insertion and deletion of nodes and edges as well as their (re-)labelling through standard text-entry.
[1] https://github.com/paulbrodersen/netgraph