What if graph algorithms could be expressed as linear algebraic operations ? And what if this translation would make graph algorithms super efficient so that this would represent a viable and scalable alternative for high-performance graph analytics ? GraphBLAS provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. In this talk, we will introduce the general concepts and the theory behind the GraphBLAS standards. We will explore practical examples using python-graphblas, i.e. the official Python API to GraphBLAS, and its integration with networkX.

GraphBLAS is a powerful and expressive standard that establishes a framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. This has several and important implications on how graph algorithms, e.g. Single Source Shortest Path (SSSP), and Page-rank network centrality, could be thought and used for scalable and high performance graph analytics.

The python-graphblas project is the official Python API to the SuiteSparse::GraphBLAS project, that is a complete implementation of the GraphBLAS standard.

In this talk, we will introduce the general concepts and the theory behind GraphBLAS, and then we will explore practical examples of graphblas-algorithms in Python, along with networkX integration.