Good discussion. It may help to separate the different threads. Business models that support network visualization: mostly, not such a great story. Customers w

Good discussion. It may help to separate the different threads. Business models that support network visualization: mostly, not such a great story. Customers want to solve problems, not just look at pictures of networks. Inevitably this drives the work toward domain-specific capabilities in areas like computer security, fraud detection or bioinformatics. It's a slippery slope. If you stay focused on core algorithms, your audience is other tool builders i.e. cost centers. Scaling up network visualization: fascinating technical problem, but a human can't actually see a million objects at once or form a mental map of their locations. So it's more like a clustering problem. Not a big surprise that Graphistry adopted uMAP. It's treating nodes more like points in big plot. We're not concerned with the same problems as illustration quality rendering of small readable graphs. Building your own: an appeal of network visualization is that you can get going by just writing some kind of physical simulation, assign reasonable coordinates to nodes, drawing edges as lines, and poof you're done. If your goal is consistently making concrete diagrams that look like a human drew them (with nodes that have shapes and ports, various kinds of labels, constraints on edge routing, nesting, aspect ratio control, etc.) there are so many intricate subproblems that you could spend years on any of them. But what's the financial incentive? The research frontier: no doubt machine learning will eventually transform this domain the way it has many others. The combinatorial objectives of network diagramming make it challenging for now. (Can an algorithm learn orthogonal planar layouts with port constraints? Maybe. Would like to see that.) Another frontier is to extend general methods for declarative 2D layouts. People don't want just pictures of networks, they want more elaborate diagrams: computer networks, metabolic pathways, business processes, cryptocurrency transactions. Network visualization is only a subproblem in information visualization. This ties in to the first point, people need to solve problems in a specific domain.