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DrTimothyAldenDavis
GraphBLAS
C2020–2025
DrTimothyAldenDavis / GraphBLAS

GraphBLAS

SuiteSparse:GraphBLAS: graph algorithms in the language of linear algebra. For production: (default) STABLE branch. Code development: ask me for the right branch before submitting a PR. video intro: https://youtu.be/Tj5y6d7FegI

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REPOSITORY AT A GLANCE

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What it is

SuiteSparse:GraphBLAS: graph algorithms in the language of linear algebra. For production: (default) STABLE branch. Code development: ask me for the right branch before submitting a PR. video intro: https://youtu.be/Tj5y6d7FegI

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Where it is used

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What people are saying

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year10 dated mentions

Observed mentions: 2020-02-06 01:00:10 UTC to 2025-11-24 10:27:11 UTC.

012342020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 4 mentions; 2 submissions, 2 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC422
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC110
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC202
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC303

What Hacker News says

10 sources
michelpp·Sep 11, 2025
Yep, for any decent sized graph, sparse is an absolute necessity, since a dense matrix will grow with the square of the node size, sparse matrices and sparse matrix multiplication is complex and there are multiple kernel approaches depending on density and other factors. SuiteSparse [1] handles these cases, has a kernel JIT compiler for different scenarios and graph operations, and supports CUDA as well. Worth checking out if you're into algebraic graph theory. Using SuiteSparse and the standard GAP benchmarks, I've loaded graphs with 6 billion edges into 256GB of RAM, and can BFS that graph in under a second. [2] [1] https://github.com/DrTimothyAldenDavis/GraphBLAS [2] https://onesparse.com/
In Adjacency Matrix and std:mdspan, C++23
michelpp·Mar 4, 2024
I think one of the elements that author is missing here is that graphs are sparse matrices, and thus can be expressed with Linear Algebra. They mention adjacency matrices, but not sparse adjacency matrices, or incidence matrices (which can express muti and hypergraphs). Linear Algebra is how almost all academic graph theory is expressed, and large chunks of machine learning and AI research are expressed in this language as well. There was recent thread here about PageRank and how it's really an eigenvector problem over a matrix, and the reality is, all graphs are matrices, they're typically sparse ones. One question you might ask is, why would I do this? Why not just write my graph algorithms as a function that traverses nodes and edges? And one of the big answers is, parallelism. How are you going to do it? Fork a thread at each ed
Read the rest
ge? Use a thread pool? What if you want to do it on CUDA too? Now you have many problems. How do you know how to efficiently schedule work? By treating graph traversal as a matrix multiplication, you just say Ax = b, and let the library figure it out on the specific hardware you want to target. Here for example is a recent question on the NetworkX repo for how to find the boundary of a triangular mesh, it's one single line of GraphBLAS if you consider the graph as a matrix: https://github.com/networkx/networkx/discussions/7326 This brings a very powerful language to the table, Linear Algebra. A language spoken by every scientist, engineer, mathematician and researcher on the planet. By treating graphs like matrices graph algorithms become expressible as mathematical formulas. For example, neural networks are graphs of adjacent layers, and the operation used to traverse from layer to layer is matrix multiplication. This generalizes to all matrices. There is a lot of very new and powerful research and development going on around sparse graphs with linear algebra in the GraphBLAS API standard, and it's best reference implementation, SuiteSparse:GraphBLAS: https://github.com/DrTimothyAldenDavis/GraphBLAS SuiteSparse provides a highly optimized, parallel and CPU/GPU supported sparse Matrix Multiplication. This is relevant because traversing graph edges IS matrix multiplication when you realize that graphs are matrices. Recently NetworkX has grown the ability to have different "graph engine" backends, and one of the first to be developed uses the python-graphblas library that binds to SuiteSparse. I'm not a directly contributor to that particular work but as I understand it there has been great results.
In The hunt for the missing data type
DocSparse·May 23, 2020
I was referring to the PageRank_semiring in https://github.com/DrTimothyAldenDavis/GraphBLAS/blob/5e569f... . I wasn't counting the test for the termination condition -- in the statement in my interview, I assumed the # of iterations could just be given. I would guess that's the case for the Google PageRank computation, since the graph doesn't change so much each month when they compute it, they probably know how many iterations they need.
In GraphBLAS – Graph algorithms in the language of linear algebra
chrisaycock·May 23, 2020
>> Google's entire PageRank computation can be done with a handful of iterations around a single call to GraphBLAS, using what I call the "PageRank semiring." The author has an example of PageRank in C: https://github.com/DrTimothyAldenDavis/GraphBLAS/blob/stable... I assume his "single call" statement refers to the MATLAB version: https://github.com/DrTimothyAldenDavis/GraphBLAS/blob/stable... for i = 1:20 r = ((c*r) * C) + a * sum (r) ; end The MATLAB interface uses overloaded operators and methods on a GraphBLAS matrix object.
In GraphBLAS – Graph algorithms in the language of linear algebra

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