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We propose GraphMineSuite (GMS): the first benchmarking suite for graphmining that facilitates evaluating and constructing high-performance graphmining algorithms. First, GMS comes with a benchmark specification based onextensive literature review, prescribing representative problems, algorithms,and datasets. Second, GMS offers a carefully designed software platform forseamless testing of different fine-grained elements of graph mining algorithms,such as graph representations or algorithm subroutines. The platform includesparallel implementations of more than 40 considered baselines, and itfacilitates developing complex and fast mining algorithms. High modularity ispossible by harnessing set algebra operations such as set intersection anddifference, which enables breaking complex graph mining algorithms into simplebuilding blocks that can be separately experimented with. GMS is supported witha broad concurrency analysis for portability in performance insights, and anovel performance metric to assess the throughput of graph mining algorithms,enabling more insightful evaluation. As use cases, we harness GMS to rapidlyredesign and accelerate state-of-the-art baselines of core graph miningproblems: degeneracy reordering (by up to >2x), maximal clique listing (by upto >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x),also obtaining better theoretical performance bounds.

GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra