Abstract: Sampling is a standard approach to big graph analytics. But
a good sample need to represent graph properties of interest with a
known degree of accuracy. This talk describes a generic tunable
sampling framework, graph sample and hold, that applies to graph
stream sampling in which edges are presented one at a time, and from
which unbiased estimators of graph properties can be produced in
post-processing. The talk also describes the performance of the method
on various types of graph, including social graphs, amongst others.
Watch the full presentation on the WNCG YouTube Channel.