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GraphBLAS for Python
Jupyter Notebook C Python C++ Objective-C Shell Dockerfile
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pygraphblas is a Python wrapper around the GraphBLAS API.


pygraphblas requires the SuiteSparse:GraphBLAS library. Once you have these installed, pygraphblas can be installed with:

python install

An installation script for Ubuntu 18.04 is provided in the file.


pygraphblas is distributed as a docker image on Docker Hub and can be run with the command:

docker run -it graphblas/pygraphblas-notebook ipython


You can run a Jupyter notebook server with docker and try the example Notebooks:


Open up the URL and see the following Notebooks:


Next run the tests:

$ ./
========================================== test session starts ==========================================
platform linux -- Python 3.7.3, pytest-5.0.1, py-1.8.0, pluggy-0.12.0
rootdir: /pygraphblas, inifile: setup.cfg
plugins: cov-2.7.1
collected 54 items

tests/ .                                                                              [  1%]
tests/ ...............................                                              [ 59%]
tests/ ....                                                                         [ 66%]
tests/ ..................                                                           [100%]

======================================= 54 passed in 1.04 seconds =======================================


pygraphblas is a python extension that bridges The GraphBLAS API with the Python programming language using the CFFI library to wrap the low level GraphBLAS API and provide high level Matrix and Vector Python types.

GraphBLAS is a sparse linear algebra API optimized for processing graphs encoded as sparse matrices and vectors. In addition to common real/integer matrix algebra operations, GraphBLAS supports up to 960 different Semiring algebra operations, that can be used as basic building blocks to implement a wide variety of graph algorithms. See Applications from Wikipedia for some specific examples.

pygraphblas leverages the expertise in the field of sparse matrix programming by The GraphBLAS Forum and uses the SuiteSparse:GraphBLAS API implementation. SuiteSparse:GraphBLAS is brought to us by the work of Dr. Tim Davis, professor in the Department of Computer Science and Engineering at Texas A&M University. News and information can provide you with a lot more background information, in addition to the references below.

While it is my goal to make it so that pygraphblas works with any GraphBLAS implementation, it currently only works with SuiteSparse v3.1.0. SuiteSparse provides several "extension" features and pre-packaged objects that are very useful for pygraphblas. If there is a GraphBLAS implementation you would like to see support for in pygraphblas, please consider sending me a pull request.


Matrices can be used as powerful representations of graphs, as described in this mathmatical introduction to GraphBLAS by Dr. Jeremy Kepner head and founder of MIT Lincoln Laboratory Supercomputing Center.

There are two useful matrix representations of graphs: Adjacency Matrices and Incidence Matrices. For this introduction we will focus on the adjacency type as they are simpler, but the same ideas apply to both, both are suported by GraphBLAS and pygraphblas, and it is easy to switch back and forth between them.

An example graph and its adjacency matrix

On the left is a graph, and on the right, the adjacency matrix that represents it. The matrix has a row and column for every node in the graph. If there is an edge going from node A to B, then there will be a value present in the intersection of As row with Bs column. How it differs from many other matrix representations is that the matrix is sparse, nothing is stored in computer memory where there are unused elements.

Sparsity is important because one practical problem with matrix-encoding graphs is that most real-world graphs tend to be sparse, as above, only 7 of 36 possible elements have a value. Those that have values tend to be scattered randomly across the matrix (for "typical" graphs), so dense linear algebra libraries like BLAS or numpy do not encode or operate on them efficiently, as the relevant data is mostly empty memory with actual data elements spaced far apart. This wastes memory and CPU resources, and defeats CPU caching mechanisms.

For example, suppose a fictional social network has 1 billion users, and each user has about 100 friends, which means there are about 100 billion (1e+11) connections in the graph. A dense matrix large enough to hold this graph would need (1 billion)^2 or (1,000,000,000,000,000,000), a "quintillion" elements, but only 1e+11 of them would have meaningful values, leaving only 0.0000001th of the matrix being utilized.

By using a sparse matrix instead of dense, only the elements used are actually stored in memory. The parts of the matrix with no value are interpreted, but not necessarily stored, as an identity value, which may or may not be the actual number zero, but possibly other values like positive or negative infinity depending on the particular semiring operations applied to the matrix.

Semirings encapsulate different algebraic operations and identities that can be used to multiply matrices and vectors. Anyone who has multiplied matrices has used at least one Semiring before, typically referred to as "plus_times". This is the common operation of multiplying two matrices containing real numbers, the corresponding row and column entries are multipled and the results are summed for the final value.

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