Vonarburg-Shmaria

Maciej Besta, Zur Vonarburg-Shmaria, Yannick Schaffner, Leonardo Schwarz, Grzegorz Kwasniewski, Lukas Gianinazzi, Jakub Beranek, Kacper Janda, Tobias Holenstein, Sebastian Leisinger, Peter Tatkowski, Esref Ozdemir, Adrian Balla, Marcin Copik, Philipp Lindenberger, Pavel Kalvoda, Marek Konieczny, Onur Mutlu, Torsten Hoefler
arXiv:2103.03653 [cs.DC] 2021

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

This paper has been accepted to PVLDB vol 14, 2021 and was presented at VLDB'21. Get the paper from arXiv.
Visit our website for documentation and the code.

Creating graph mining algorithms made simple

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, pre-scribing 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>2×)
  * maximal clique listing (by up to >9×)
  * 𝑘-clique listing (by 1.1×)
  * subgraph isomorphism (by up to 2.5×)