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LAGraph: Linear Algebra, Network Analysis Libraries, and the Study of Graph Algorithms

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arxiv 2104.01661 v1 pith:LNXRPFUA submitted 2021-04-04 cs.MS cs.DS

Gábor Szárnyas , David A. Bader , Timothy A. Davis , James Kitchen , Timothy G. Mattson , Scott McMillan , Erik Welch This is my paper · ORCID
classification cs.MScs.DS
keywords algorithmslagraphlibrarygraphgraphblasalgebraanalysisbenchmark
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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.

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  1. Single-Source Regular Path Querying in Terms of Linear Algebra

    cs.DS 2024-12 conditional novelty 6.0 of 10

    LARPQ evaluates two-way regular path queries by expressing the simultaneous traversal of graph and query automaton as Boolean linear algebra, with a correctness proof and a mixed but often favorable performance evaluation.

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