Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/Graphegon/pygraphblas

GraphBLAS for Python
https://github.com/Graphegon/pygraphblas

graph-algorithms graph-analysis graphblas graphs ipynb linear-algebra matrix python semirings suitesparse

Last synced: about 2 months ago
JSON representation

GraphBLAS for Python

Awesome Lists containing this project

README 

        
![Tests](https://github.com/Graphegon/pygraphblas/workflows/Tests/badge.svg)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Graphegon/pygraphblas/v4.0.3?filepath=pygraphblas%2Fdemo%2FIntroduction-to-GraphBLAS-with-Python.ipynb)




# pygraphblas



pygraphblas is a Python wrapper around the

[GraphBLAS](http://graphblas.org) API.



## [Click here for API Documentation](https://graphegon.github.io/pygraphblas/pygraphblas/index.html)



## Installation



pygraphblas requires the

[SuiteSparse:GraphBLAS](http://faculty.cse.tamu.edu/davis/GraphBLAS.html)

library.  The easiest way currently to install pygraphblas on Linux is

to use pip:



    pip install pygraphblas



This is currently only supported on Linux but Windows and MacOS binary

packages are currently a work in progress.



To install from source, first install SuiteSparse, then run:



    python setup.py install

  

There are two ways to download precompiled binaries of pygraphblas

with SuiteSparse included.  One way is to use `pip install

pygraphblas` on an Intel Linux machine.  This will download a package

compatible with most modern linux distributions.  This also works in a

Docker container on Mac.



There are also pre-build docker images based on Ubuntu 20.04 that have

a pre-compiled SuiteSparse and pygraphblas installed.  These come in

two flavors `minimal` which is the Ipython interpreter-only, and

`notebook` which comes with a complete Jupyter Notebook server.  These

containers also work on Mac.



An installation script for Ubuntu 18.04 is provided in the

`install-ubuntu.sh` file.



NOTE: DO NOT USE THESE PRE-COMPILED BINARIES FOR BENCHMARKING

SUITESPARSE.  These binaries are not guaranteed to be idealy compiled

for your environment.



## Docker 



pygraphblas is distributed as two different docker images on [Docker

Hub](https://cloud.docker.com/repository/docker/pygraphblas/pygraphblas/general)

.  The "minimal" image, containing only the library and

[ipython](https://ipython.org/) and can be run with the command:



    docker run --rm -it graphblas/pygraphblas-minimal ipython



You can run a "full" [Jupyter notebook](https://jupyter.org/) server

with docker and try the example Notebooks use the command:



    docker run --rm -it -p 8888:8888 graphblas/pygraphblas-notebook



Open up the URL printed on your terminal screen to see the demo

Notebook folder, including:



 - [Introduction to GraphBLAS with Python](./demo/Introduction-to-GraphBLAS-with-Python.ipynb)

 - [PageRank](./demo/PageRank.ipynb)

 - [Betweeness Centrality](./demo/BetweenessCentrality.ipynb)

 - [Triangle Centrality](./demo/TriangleCentrality.ipynb)

 - [Gallery of Centrality](./demo/Centrality.ipynb)

 - [K-Truss Subgraphs](./demo/K-Truss.ipynb)

 - [Triangle Counting](./demo/Triangle-Counting.ipynb)

 - [Louvain Community Detection](./demo/Louvain.ipynb)

 - [RadiX-Net Topologies](./demo/RadiX-Net-with-pygraphblas.ipynb)

 - [User Defined Types](./demo/User-Defined-Types.ipynb)

 - [Log Semiring Type](./demo/Log-Semiring.ipynb)



# Tests



To run the tests checkout pygraphblas and use:



    $ ./test.sh



# Summary



pygraphblas is a python extension that bridges [The GraphBLAS

API](http://graphblas.org) with the [Python](https://python.org)

programming language.  It uses the

[CFFI](https://cffi.readthedocs.io/en/latest/) library to wrap the low

level GraphBLAS API and provides high level Matrix and Vector Python

types that make GraphBLAS simple and easy.



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 over a

thousand different [Semiring](https://en.wikipedia.org/wiki/Semiring)

algebra operations, that can be used as basic building blocks to

implement a wide variety of graph algorithms. See

[Applications](https://en.wikipedia.org/wiki/Semiring#Applications)

from Wikipedia for some specific examples.



pygraphblas leverages the expertise in the field of sparse matrix

programming by [The GraphBLAS Forum](http://graphblas.org) and uses

the

[SuiteSparse:GraphBLAS](http://faculty.cse.tamu.edu/davis/GraphBLAS.html)

API implementation. SuiteSparse:GraphBLAS is brought to us by the work

of [Dr. Tim Davis](http://faculty.cse.tamu.edu/davis/welcome.html),

professor in the Department of Computer Science and Engineering at

Texas A&M University.  [News and

information](http://faculty.cse.tamu.edu/davis/news.html) 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.

SuiteSparse is currently the only realistically usable GraphBLAS

implementation, and additionally it 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 creating an issue for

it for discussion and/or sending me a pull request.



# Introduction to Graphs and Matrices



GraphBLAS uses matrices and Linear Algebra to represent graphs, as

described [in this mathmatical introduction to

GraphBLAS](http://www.mit.edu/~kepner/GraphBLAS/GraphBLAS-Math-release.pdf)

by [Dr. Jeremy Kepner](http://www.mit.edu/~kepner/) head and founder

of [MIT Lincoln Laboratory Supercomputing

Center](http://news.mit.edu/2016/lincoln-laboratory-establishes-supercomputing-center-0511).



There are two useful matrix representations of graphs: [Adjacency

Matrices](https://en.wikipedia.org/wiki/Adjacency_matrix) and

[Incidence Matrices](https://en.wikipedia.org/wiki/Incidence_matrix).

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 adjacency matrix](./docs/imgs/AdjacencyMatrixBFS.png)



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.



## Code of Conduct



Everyone interacting in the pygraphblas project's codebases, issue

trackers, chat rooms, and mailing lists is expected to follow the [PSF

Code of Conduct](https://www.python.org/psf/conduct/).