Graphs, Matrices, and the GraphBLAS: Seven Good Reasons

TL;DR

The paper introduces the GraphBLAS standard, which uses matrix-based graph algorithms to address challenges in graph analysis such as complexity, security, and performance, enabling broader application and efficiency.

Contribution

It presents the GraphBLAS standard, defining core matrix operations for graph algorithms to simplify implementation and improve performance across diverse environments.

Findings

Defines a set of core matrix operations for graph algorithms

Addresses key challenges in graph analysis with matrix-based methods

Facilitates implementation of efficient, scalable graph algorithms

Abstract

The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istc-bigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically 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 GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.