WNCG Seminar Series: Sampling Big Graphs and Streams
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.
Speaker
Nick Duffield is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. From 2013 until 2014, he was a Research Professor at DIMACS (the Center for Discrete Mathematics and Theoretical Computer Science) at Rutgers University, New Jersey, USA. From 1995 until 2013, he worked at AT&T Labs-Research where he was a Distinguished Member of Technical Staff and an AT&T Fellow.
Prof. Duffield works on the acquisition, analysis and applications of Big Data to communication networks and beyond.