So I have seen many of these kinds of graphs and associated complaints that come with them. Much of the criticism revolves around the usefulness of ``popularity

So I have seen many of these kinds of graphs and associated complaints that come with them. Much of the criticism revolves around the usefulness of ``popularity'' as a proxy for other things that we actually care about in programming languages having to do with their immediate usefulness to us. These are all good to discuss, but I think there is a more pressing issue, having to do with the granularity of the sample. I work in an area of scientific computing, and have 1-2 languages at any given time that I work with often, and have familiarity with 3-4 more. However, I have friends that work in slightly different areas of scientific computing that have a different set of languages. Integrating information from these people, I have a rough set of the languages used in my local neighborhood. And I will say there is actually quite a bit of diversity in the neighborhood, even though its all within scientific computing. So who is to say what lies in the realm of friend of a friend of a friend? I don't have a feel for this distribution. This is where I think integrating these kinds of graphs with a filtration over distances on some network would be highly useful. It is not clear which network(s) would be the best, but there are some clear advantages to going with induced link through github collaborations. What I am suggesting might be more helpful for niche domains than it would be for the ``median'' programmer (if such a thing exists), but who knows, maybe there is some usefulness there too?