GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
This provides a programmable framework for researchers and practitioners to evaluate and accelerate graph mining algorithms, though it is incremental as it builds on existing methods with new tools.
The authors tackled the lack of a standardized benchmarking suite for graph mining algorithms by introducing GraphMineSuite (GMS), which includes a benchmark specification, a modular software platform with over 40 baseline implementations, and set algebra operations, resulting in speedups of up to >9x for maximal clique listing and up to 2.5x for subgraph isomorphism.
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by up to >2x), maximal clique listing (by up to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x), also obtaining better theoretical performance bounds.