That said, multi-terabyte memories won’t solve interesting problems; we already had that. When I was working on this 15-ish years ago, the real-world data models had trillions of vertices, never mind edges. And that has only gotten larger with time. A lot of the research ended up focusing on the problem of how do you boil the ocean selectively and incrementally to optimize throughput.
There is no way to trivially throw hardware at the problem; graph-cutting is hard, and you have to do it even within single servers. Even with sophisticated latency-hiding, it ends up being about effective bandwidth in a context where caches are almost useless.
For graph analysis specifically, we could do a lot with big servers, this is true. But it would require a completely different software architecture to the way most graph analysis is done now. This is perpetually on my “copious spare time” lists of projects because there is a big gap here.