"Efficient processing of large graphs is challenging. Graph
algorithms often exhibit poor locality of memory access, very
little work per vertex, and a changing degree of parallelism
over the course of execution [31, 39]. Distribution over many
machines exacerbates the locality issue, and increases the
probability that a machine will fail during computation."
https://kowshik.github.io/JPregel/pregel_paper.pdf
^ (Pregel is Google's batch graph processing framework)
FWIW most graph computations can fit on a single powerful machine these days. There really aren't that many that require distributed processing.