The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. Mathematically the Graph- BLAS 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 mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, the two are easily connected by matrix mul- tiplication. A k...
Copyright 2015 ACM. As new applications for graph algorithms emerge, there has been a great deal of ...
This new edition illustrates the power of linear algebra in the study of graphs. The emphasis on mat...
Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, gr...
The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based gra...
The analysis of graphs has become increasingly important to a wide range of applications. Graph anal...
AbstractThe analysis of graphs has become increasingly important to a wide range of applications. Gr...
The analysis of graphs has become increasingly important to a wide range of applications. Graph anal...
This tutorial describes the theoretical background of GraphBLAS. First, we discuss the need for a st...
The challenges associated with graph algorithm scaling led multiple scientists to identify the need ...
Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a library of low-level bu...
Graph algorithms typically have very low computational intensities, hence their execution times are ...
Optimizing linear algebra operations has been a research topic for decades. The compact languag...
High-performance implementations of graph algorithms are challenging to implement on new parallel ha...
It is our view that the state of the art in constructing a large collection of graph algorithms in t...
This paper describes a program that was written to help students better learn the material on the gr...
Copyright 2015 ACM. As new applications for graph algorithms emerge, there has been a great deal of ...
This new edition illustrates the power of linear algebra in the study of graphs. The emphasis on mat...
Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, gr...
The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based gra...
The analysis of graphs has become increasingly important to a wide range of applications. Graph anal...
AbstractThe analysis of graphs has become increasingly important to a wide range of applications. Gr...
The analysis of graphs has become increasingly important to a wide range of applications. Graph anal...
This tutorial describes the theoretical background of GraphBLAS. First, we discuss the need for a st...
The challenges associated with graph algorithm scaling led multiple scientists to identify the need ...
Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a library of low-level bu...
Graph algorithms typically have very low computational intensities, hence their execution times are ...
Optimizing linear algebra operations has been a research topic for decades. The compact languag...
High-performance implementations of graph algorithms are challenging to implement on new parallel ha...
It is our view that the state of the art in constructing a large collection of graph algorithms in t...
This paper describes a program that was written to help students better learn the material on the gr...
Copyright 2015 ACM. As new applications for graph algorithms emerge, there has been a great deal of ...
This new edition illustrates the power of linear algebra in the study of graphs. The emphasis on mat...
Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, gr...