Visualizing large graphs is a natural desire for people with lots of connected data. But after a fairly small size, there's almost no utility in visualizing graphs. It's much more useful to compute various measures on the graph, and then query the graph using some combination of node/edge values and these computed values. You might subset out the nodes and edges of particular interest if you really want to see them -…
Can you recommend any good literature on the subject?
0 - https://www.cs.purdue.edu/homes/xmt/classes/slides/CS530/Inf...
- https://en.wikipedia.org/wiki/Data_and_information_visualiza...
There used to be a lively research field for information visualization that studied current visualization techniques and proposed new ones to solve specific challenges -- I remember when treemaps were first introduced for example [1]. Large networks were a pretty big area of research at the time with all kinds of centrality clustering, and edge minimization techniques.
1 - https://www.google.com/search?q=treemap+visualization&tbs=im...
A few teams even tried various kind of hyperbolic representations [2,3] so that areas under local inspection were magnified under your cursor, and the rest of the hairball was pushed off to the edges of the display. But with big graphs you run into quite a few big problems very quickly like local vs. global visibility, layout challenges, etc.
2 - https://graphics.stanford.edu/papers/webviz/webviz/node2.htm...
3 - https://www.caida.org/catalog/software/walrus/
Not specifically graph related, but the best critical thinker I know of in the space is probably Edward Tufte [4]. I have some problems with a few bits of his thinking, and other than sparklines his contributions are mostly in terms of critically challenging what should be represented, why, how, and methods of interaction, his critical analysis has stayed up there as some of the best. He has a book set that's a really great collection of his thoughts.
4 - https://www.edwardtufte.com/tufte/
If you approach this problem critically, you end up at the inevitable conclusion that trying to globally visualize a massive graph in general is basically useless. Sure there are specific topologies that can be abstracted into easier to display graphs, but the general case is not conducive. It's also somewhat surprising at how small a graph can be before visualizing it gets out of hand -- maybe a few dozen nodes and edges.
I remember the U.S. DoE did some really pioneering studies in the field and produced some underappreciated experts like Thomas, Cook and Risch [5,6]. I like Risch's concepts around visualizations as formal metaphors of data. I think he's successful in defining the rigorous atomic components of visualization that you can build up from. Considering OP's request in view of Tufte and Risch, I think that they really need to think about the potential for different metaphors at different levels of detail (since they specify zooming in and out). There may not exist a single metaphor that can visualize certain data at every conceivable scope and detail!
5 - https://ils.unc.edu/courses/2017_fall/inls641_001/books/RD_A...
6 - https://arxiv.org/pdf/0809.0884v1
One interesting artifact from all of this is that most of the research has long ago been captured and commoditized or made open source. There really isn't a market anymore for commercial visualization companies, or grant money for visualization research. D3.js [7] (and the derivatives) more or less took millions upon millions of dollars in R&D and commercial research and boiled it down into a free, open source, library that captured pretty much all of the major findings in one place. It's objectively better than anything that was on the market or in labs at the time I was in the space and it's free.