It's kind of a mysterious art, and I too mostly rely on scientific papers. It's a surprisingly small field and you need to get into the habit of chasing down citations in papers, because many important ideas got laid out long before the computer power existed to realize them at scale. Sometimes a 20-30 year old paper of only a few pages has the actual algorithm, and it's so well known in the field that it no longer stands out in more recent papers.

Here's a few useful references:

Good overview on big graphs: https://towardsdatascience.com/large-graph-visualization-too...

A gallery of large graphs - horrid user interface, but you can click through and find an absolute wealth of resources. Curated by Yifan Hu, who developed one of the popular layout algorithms: http://yifanhu.net/GALLERY/GRAPHS/

Graphviz is a very well-documented library with a lot of the 'classic' layouts.

Astronomy, physics, and bio people have a lot of useful visualization tools and techniques for huge datasets, but you will have to go looking for them - not because they don't like to share, but because they mostly write to each other so you won't just land on stuff by browsing Github. Absolute must-have literature review: https://arxiv.org/abs/2110.01866

A lot of large graph visualization techniques are about using simple graph visualization techniques but first combing out the hairballs through the application of dimensionality reduction, motif extraction, backbone identification and so on. This is an important paper whose techniques have yet to be fully explored: https://jgaa.info/accepted/2015/NocajOrtmannBrandes2015.19.2...

For a combination of theoretical and practical reasons, most visualization zeroes in on rendering smallish graphs in 2 dimensions. Large graphs are either so densely connected as to be be intractable (the brain being the ultimate hairball) or so sparse as be like digital planetariums - gorgeous, impressive, and looking much the same in every direction.

I could go on at length but as you can maybe guess I'm a consumer of other people's research rather than an expert in implementing the fundamentals. Also I don't have any academic background whatsoever so I apologize for the haphazard infodump. I've been studying/applying stuff from this field for ~15 years but it's too out there for most people. Feel free to email though.