Mathematical Foundations of the GraphBLAS
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Abstract (arXiv)
The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. Mathematically the Graph- BLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, the two are easily connected by matrix mul- tiplication. A key feature of matrix mathematics is that a very small number of matrix operations can be used to manipulate a very wide range of graphs. This composability of small number of operations is the foundation of the GraphBLAS. A standard such as the GraphBLAS can only be effective if it has low performance overhead. Performance measurements of prototype GraphBLAS implementations indicate that the overhead is low.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jun 2016.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 0; 2024: 2; 2025: 0; 2026: 4.
12 papers.
- 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
- 2021Motif Prediction with Graph Neural Networkscited by 18
- 2022Neural Graph Databasescited by 11
- 2021GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebracited by 10
- 2021Functional Collection Programming with Semi-Ring Dictionariescited by 6
- 2026Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Acceleratorscited by 6
- 2024Demystifying Higher-Order Graph Neural Networkscited by 4
- 2026Parallelizing Counterfactual Regret Minimizationcited by 2
- 2021Maneuver Identification Challengecited by 0
- 2024GPU-Accelerated Counterfactual Regret Minimizationcited by 0
- 2026Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simplecited by 0
- 2026LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrievalcited by 0
- 2026Parallelizing Counterfactual Regret Minimization14 May 2026
- 2026LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval20 Apr 2026
- 2026Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators26 Feb 2026
- 2026Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simple22 Jan 2026
- 2024GPU-Accelerated Counterfactual Regret Minimization27 Aug 2024
- 2024Demystifying Higher-Order Graph Neural Networks18 Jun 2024
- 2022Neural Graph Databases20 Sep 2022
- 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis19 May 2022
- 2021Maneuver Identification Challenge25 Aug 2021
- 2021Motif Prediction with Graph Neural Networks26 May 2021
- 2021Functional Collection Programming with Semi-Ring Dictionaries10 Mar 2021
- 2021GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra5 Mar 2021