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Important graph mining problems such as Clustering are computationallydemanding. To significantly accelerate these problems, we propose ProbGraph: agraph representation that enables simple and fast approximate parallel graphmining with strong theoretical guarantees on work, depth, and result accuracy.The key idea is to represent sets of vertices using probabilistic setrepresentations such as Bloom filters. These representations are much faster toprocess than the original vertex sets thanks to vectorizability and small size.We use these representations as building blocks in important parallel graphmining algorithms such as Clique Counting or Clustering. When enhanced withProbGraph, these algorithms significantly outperform tuned parallel exactbaselines (up to nearly 50x on 32 cores) while ensuring accuracy of more than90% for many input graph datasets. Our novel bounds and algorithms based onprobabilistic set representations with desirable statistical properties are ofseparate interest for the data analytics community.

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