ASAP: fast, approximate graph pattern mining at scale
ASAP: fast, approximate graph pattern mining at scale Iyer et al., OSDI’18
I have a real soft spot for approximate computations. In general, we waste a lot of resources on overly accurate analyses when understanding the trends and / or the neighbourhood is quite good enough (do you really need to know it’s 78.763895% vs 78 ± 1%?). You can always drill in with more accuracy if the approximate results hint at something interesting or unexpected.
Approximate analytics is an area that has gathered attention in big data analytics, where the goal is to let the user trade-off accuracy for much faster results.
(See e.g. ApproxHadoop which we covered on The Morning Paper a while back).
In the realm of graph processing, graph pattern mining algorithms, which discover structural patterns in a graph, can reveal very interesting things in our data but struggle to scale to larger graphs. This is in contrast to graph analysis algorithms such as PageRank which typically compute properties of a graph using neighbourhood information.
Today, a deluge of graph processing frameworks exist, both in academia and open-source… a vast majority of the existing graph processing frameworks however have focused on graph Continue reading




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