[Paper Review] GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra

Maciej Besta, Zur Vonarburg-Shmaria+16 more|arXiv (Cornell University)|Mar 5, 2021

Graph Theory and Algorithms201 references5 citations

TL;DR

GraphMineSuite (GMS) is a high-performance, programmable benchmarking suite for graph mining that leverages set algebra operations—such as intersection and difference—to modularize and optimize complex algorithms. It enables up to 9× speedup in maximal clique listing and 2.5× in subgraph isomorphism by systematically evaluating and accelerating state-of-the-art baselines through a unified platform with novel performance metrics and concurrency analysis.

ABSTRACT

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.

Motivation & Objective

• To address the lack of standardized, high-performance evaluation frameworks for graph mining algorithms.
• To reduce the complexity of algorithmic design choices—such as graph representations, reorderings, and data structures—by enabling modular experimentation.
• To provide a portable, extensible platform that supports both performance benchmarking and theoretical concurrency analysis.
• To accelerate state-of-the-art graph mining algorithms through systematic optimization using set algebra abstractions.
• To introduce a novel performance metric, 'algorithmic throughput,' for more insightful evaluation beyond raw runtimes.

Proposed method

• Design a benchmark specification based on a comprehensive literature review of representative graph mining problems, algorithms, and datasets.
• Implement a software platform that supports fine-grained experimentation with graph representations, algorithm subroutines, and optimizations via set algebra operations.
• Provide parallel implementations of 40+ baseline algorithms, including optimized variants of Bron-Kerbosch, degeneracy reordering, and subgraph isomorphism.
• Integrate set algebra primitives (e.g., intersection, difference) as first-class abstractions to decompose complex algorithms into composable, testable components.
• Develop a novel performance metric—'algorithmic throughput'—to assess mining efficiency independent of input size and hardware scale.
• Conduct a theoretical concurrency analysis to provide insights into algorithmic scalability and portability across architectures.

Experimental results

Research questions

• RQ1 How can graph mining algorithms be systematically evaluated and compared in a standardized, high-performance framework?
• RQ2 To what extent can set algebra operations serve as a unifying abstraction for designing and optimizing diverse graph mining workloads?
• RQ3 Can a modular, composable platform significantly reduce the engineering effort required to explore design trade-offs in graph representations and algorithmic components?
• RQ4 How does the proposed 'algorithmic throughput' metric improve evaluation beyond traditional wall-clock runtimes?
• RQ5 What performance gains can be achieved by applying systematic optimizations—such as approximate degeneracy reordering and result caching—within a unified benchmarking environment?

Key findings

• The Bron-Kerbosch algorithm was accelerated by up to 9× in maximal clique listing through optimizations like approximate degeneracy reordering and result caching.
• Degeneracy reordering reduced the runtime of the Bron-Kerbosch algorithm by more than 2× compared to baseline implementations.
• Subgraph isomorphism performance improved by up to 2.5× using GMS-optimized variants, with better theoretical work bounds.
• The k-clique listing algorithm achieved a 1.1× speedup, demonstrating the platform’s utility even for less dramatic performance gains.
• The novel 'algorithmic throughput' metric enabled more insightful performance comparisons by normalizing for input size and hardware scale.
• The theoretical concurrency analysis provided deeper insights into algorithmic scalability, supporting portability across diverse parallel architectures.