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

arXiv: https://arxiv.org/abs/2103.03653 · HTML: https://mlsanity.com/paper/2103.03653/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 223 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 30 of 50)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Red-blue pebbling revisited: near optimal parallel matrix-matrix multiplication](https://mlsanity.com/paper/1908.09606/) (2019)
- [Transformations of High-Level Synthesis Codes for High-Performance Computing](https://mlsanity.com/paper/1805.08288/) (2018)
- [Graph Summarization Methods and Applications: A Survey](https://mlsanity.com/paper/1612.04883/) (2016)
- [Local Graph Clustering with Network Lasso](https://mlsanity.com/paper/2004.12199/) (2020)
- [The GAP Benchmark Suite](https://mlsanity.com/paper/1508.03619/) (2015)
- [Mathematical Foundations of the GraphBLAS](https://mlsanity.com/paper/1606.05790/) (2016)
- [Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations](https://mlsanity.com/paper/1806.01799/) (2018)
- [Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems](https://mlsanity.com/paper/1912.12740/) (2019)
- [Scaling betweenness centrality using communication-efficient sparse matrix multiplication](https://mlsanity.com/paper/1609.07008/) (2016)
- [To Push or To Pull: On Reducing Communication and Synchronization in Graph Computations](https://mlsanity.com/paper/2010.16012/) (2020)
- [SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing](https://mlsanity.com/paper/2012.14132/) (2020)
- [Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons](https://mlsanity.com/paper/1911.04200/) (2019)
- [Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics](https://mlsanity.com/paper/1912.08950/) (2019)
- [A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning](https://mlsanity.com/paper/1901.10183/) (2019)
- [BigDataBench: a Big Data Benchmark Suite from Internet Services](https://mlsanity.com/paper/1401.1406/) (2014)
- [High-Performance Parallel Graph Coloring with Strong Guarantees on Work, Depth, and Quality](https://mlsanity.com/paper/2008.11321/) (2020)
- [Slim Fly: A Cost Effective Low-Diameter Network Topology](https://mlsanity.com/paper/1912.08968/) (2019)
- [Accelerating Irregular Computations with Hardware Transactional Memory and Active Messages](https://mlsanity.com/paper/2010.09135/) (2020)
- [SlimSell: A Vectorizable Graph Representation for Breadth-First Search](https://mlsanity.com/paper/2010.09913/) (2020)
- [Substream-Centric Maximum Matchings on FPGA](https://mlsanity.com/paper/2010.14684/) (2020)
- [From Theory to Practice: Plug and Play with Succinct Data Structures](https://mlsanity.com/paper/1311.1249/) (2013)
- [Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable](https://mlsanity.com/paper/1805.05208/) (2018)
- [Graph Processing on FPGAs: Taxonomy, Survey, Challenges](https://mlsanity.com/paper/1903.06697/) (2019)
- [Log(Graph): A Near-Optimal High-Performance Graph Representation](https://mlsanity.com/paper/2010.15879/) (2020)
- [Enabling Highly-Scalable Remote Memory Access Programming with MPI-3 One Sided](https://mlsanity.com/paper/2001.07747/) (2020)
- [Peregrine: A Pattern-Aware Graph Mining System](https://mlsanity.com/paper/2004.02369/) (2020)
- [Slim NoC: A Low-Diameter On-Chip Network Topology for High Energy Efficiency and Scalability](https://mlsanity.com/paper/2010.10683/) (2020)
- [Engineering Parallel Algorithms for Community Detection in Massive Networks](https://mlsanity.com/paper/1304.4453/) (2013)

## Built on by (most cited first, 10 of 10)

- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [Demystifying Chains, Trees, and Graphs of Thoughts](https://mlsanity.com/paper/2401.14295/) (2024)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Motif Prediction with Graph Neural Networks](https://mlsanity.com/paper/2106.00761/) (2021)
- [Neural Graph Databases](https://mlsanity.com/paper/2209.09732/) (2022)
- [HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers](https://mlsanity.com/paper/2311.18526/) (2023)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [GraphSeek: Next-Generation Graph Analytics with LLMs](https://mlsanity.com/paper/2602.11052/) (2026)
- [The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering](https://mlsanity.com/paper/2411.10290/) (2024)
- [Higher-Order Graph Databases](https://mlsanity.com/paper/2506.19661/) (2025)

## Abstract (arXiv)

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.