Yeah, more-and-more I am inclining building ML project for graph visualization.

I have worked on graph visualisation for some time, did https://github.com/nikolaydubina/jsonl-graph and https://github.com/nikolaydubina/go-graph-layout

Been studying research papers on graph visualization.

It looks like we need some Deep Learning / ML based approach to this.

There is just so much meaning is encoded into XY coordinates and edges. Basic algorithms like Sugiyama produce meaningful visualizations only for simple and basic graphs.

When number of edges goes to the roof or nodes.. basic algorithms break down. Graphs become meaningless.

You have to make graphs by hand to make sense of it.

You are drawing large graphs by hand?

ML approaches to graph layout are very exciting, since the constraints people need in real life diagrams are quite intricate, but it's still a research problem.

yes. I would scale, zoom, adjust opacity, etc. Grouping into large/smaller clusters. Typically manual work is to do groups, their shapes, connections, how to highlight important info with colors or symbols and general meaning of X and Y axis. Very similar to drawing on a whiteboard. Whatever code I run, it is often very customized to make something exactly what I want.

ML would def benefits here to capture all these heuristics.