How would you calculate something like a Hausdorff dimension of a graph given there’s no unique embedding in Euclidean space, but we still think of (for eg) bushy vs sparse branching etc? A long skinny DAG with few branches should be lower dim than dense random graph for eg 
If you squint at a (biological) tree or cauliflower it looks like a sankey diagram of an aggregate flow. The coarse topology of that flow given a resolution level. Like a recursive approximation of min-cut-max-flow view. DAGs are the easiest case.
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Specific application: “thickness” of time history of a software project in github, given graph of forks, merges, PRs etc
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https://en.m.wikipedia.org/wiki/Fractal_dimension_on_networks … This is just weird. All of these methods take abstract graphs and then *embed* them in e.g. a Euclidean space. Gotta be a better way.
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