NSDI '23 - Arya: Arbitrary Graph Pattern Mining with Decomposition-based Sampling

Arya: Arbitrary Graph Pattern Mining with Decomposition-based Sampling

Zeying Zhu, Boston University; Kan Wu, University of Wisconsin-Madison; Zaoxing Liu, Boston University

Graph pattern mining is compute-intensive in processing massive amounts of graph-structured data. This paper presents Arya, an ultra-fast approximate graph pattern miner that can detect and count arbitrary patterns of a graph. Unlike all prior approximation systems, Arya combines novel graph decomposition theory with edge sampling-based approximation to reduce the complexity of mining complex patterns on graphs with up …

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