Large-Scale Machine Learning and Graphs by Carlos Guestrin.
The presentation starts with a history of the evolution of GraphLab, which is interesting in and of itself.
Carlos then goes beyond a history lesson and gives a glimpse of a very exciting future.
Such as: installing GraphLab with Python, using Python for local development, running the same Python with Graphlab in the cloud.
Thought that might catch your eye.
Something to remember when people talk about scaling graph analysis.
If you are interested in seeing one possible future of graph processing today, not some day, check out: GraphLab Notebook (Beta).
BTW, Carlos mentions a technique call “think like a vertex” which involves distributing vertexes across machines rather than splitting graphs on edges.
Seems to me that would work to scale the processing of topic maps by splitting topics as well. Once “merging” has occurred on different machines, then “merge” the relevant topics back together across machines.