@inproceedings{1a2324a3b33d4ccda2d72b3dfc09a96d,
title = "LAGraph: Linear Algebra, Network Analysis Libraries, and the Study of Graph Algorithms",
abstract = "Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a library of low-level building blocks for such algorithms that targets algorithm developers. LAGraph builds on top of the GraphBLAS to target users of graph algorithms with high-level algorithms common in network analysis. In this paper, we describe the first release of the LAGraph library, the design decisions behind the library, and performance using the GAP benchmark suite. LAGraph, however, is much more than a library. It is also a project to document and analyze the full range of algorithms enabled by the GraphBLAS. To that end, we have developed a compact and intuitive notation for describing these algorithms. In this paper, we present that notation with examples from the GAP benchmark suite.",
keywords = "Graph Algorithms, Graph Analytics, Graph Processing, GraphBLAS, Linear Algebra",
author = "Gabor Szarnyas and Bader, {David A.} and Davis, {Timothy A.} and James Kitchen and Mattson, {Timothy G.} and Scott McMillan and Erik Welch",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 ; Conference date: 17-05-2021",
year = "2021",
month = jun,
doi = "10.1109/IPDPSW52791.2021.00046",
language = "English (US)",
series = "2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "243--252",
booktitle = "2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021",
address = "United States",
}