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.

中文翻译：

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GraphMineSuite：使用集合代数实现高性能和可编程的图挖掘算法

我们提出了GraphMineSuite（GMS）：第一个用于图形挖掘的基准测试套件，它有助于评估和构建高性能的图形挖掘算法。首先，GMS带有基于大量文献回顾的基准规范，规定了代表性问题，算法和数据集。其次，GMS提供了精心设计的软件平台，用于无缝测试图形挖掘算法的不同细粒度元素，例如图形表示或算法子例程。该平台包括40多个考虑到的基线的并行实现，并且有助于开发复杂而快速的挖掘算法。通过利用集合代数运算（例如集合相交和差），可以实现高度模块化 这样可以将复杂的图形挖掘算法分解为可以单独进行实验的简单构建块。GMS受到广泛的并发分析支持，以提高性能见解的可移植性，并提供了一种新颖的性能指标来评估图形挖掘算法的吞吐量，从而可以进行更深入的评估。作为用例，我们利用GMS来快速重新设计并加快核心图挖掘问题的最新基线：简并性重新排序（最多2倍），最大集团列表（最多9倍），k-clique列表（1.1倍）和子图同构（最大2.5倍），也可以获得更好的理论性能界限。以及一种新颖的性能指标来评估图挖掘算法的吞吐量，从而可以进行更有洞察力的评估。作为用例，我们利用GMS来快速重新设计并加快核心图挖掘问题的最新基线：简并性重新排序（最多2倍），最大集团列表（最多9倍），k-clique列表（1.1倍）和子图同构（最大2.5倍），也可以获得更好的理论性能界限。以及一种新颖的性能指标来评估图挖掘算法的吞吐量，从而可以进行更有洞察力的评估。作为用例，我们利用GMS来快速重新设计并加快核心图挖掘问题的最新基线：简并性重新排序（最多2倍），最大集团列表（最多9倍），k-clique列表（1.1倍）和子图同构（最大2.5倍），也可以获得更好的理论性能界限。