jsonl-graph VS boxesandarrows

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 can position everything manually exactly where you want it in graphviz without any of those tricks by using the neato engine.

I use scripts to manually place everything from data in csv files.

https://github.com/mathew-j-davis/boxesandarrows

I've been making diagrams using graphviz, but setting the positions and edge curves manually, pulling the nodes and edges out of a list in excel

https://github.com/mathew-j-davis/boxesandarrows

I've been setting the 'waypoints' for more complex curves (curves with many bends) manually (typing the bezier numbers in by hand), while I try and figure out how I want them routed, you're right it's not easy figuring out rules for where edge should go, let alone implementing it