Technical Papers Archive

ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations

Authors: Maciej Besta (ETH Zürich); Cesare Miglioli (University of Geneva, Switzerland); Paolo Sylos Labini (Free University of Bozen-Bolzano, Italy); Jakub Tětek (University of Copenhagen); Patrick Iff (ETH Zürich); Raghavendra Kanakagiri (University of Illinois); Saleh Ashkboos (ETH Zürich); Kacper Janda (AGH University of Science and Technology, Krakow, Poland); Michal Podstawski (Warsaw University of Technology); Grzegorz Kwasniewski and Niels Gleinig (ETH Zürich); Flavio Vella (University of Trento, Italy); and Onur Mutlu and Torsten Hoefler (ETH Zürich)

Abstract: Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enables simple and fast approximate parallel graph mining with strong theoretical guarantees on work, depth, and result accuracy. The key idea is to represent sets of vertices using probabilistic set representations such as Bloom filters. These representations are much faster to process than the original vertex sets thanks to vectorizability and small size. We use these representations as building blocks in important parallel graph mining algorithms such as Clique Counting or Clustering. When enhanced with ProbGraph, these algorithms significantly outperform tuned parallel exact baselines (up to nearly 50x on 32 cores) while ensuring accuracy of more than 90% for many input graph datasets. Our novel bounds and algorithms based on probabilistic set representations with desirable statistical properties are of separate interest for the data analytics community.




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