I'm a PhD student in the database group at the University of Washington, Seattle. I work on discovering new techniques to accelerate data management and make its results more trustworthy.
Graphs (aka networks) are a key part of the data science pipeline at many organizations. However, scalability is the most frequently reported limitation by graph analysts. I introduce
QuasiStableColors.jl, a Julia library for approximate graph analysis. On tasks such as ranking node importance (centrality) it enables an over 10x speedup while introducing less than 5% error. In this talk, I will demonstrate how to use this novel graph compression for your own workloads.