Exploiting Computation-Friendly Graph Compression Methods for Adjacency-Matrix Multiplication

Francisco, Alexandre P.; Gagie, Travis; Ladra, Susana; Navarro, Gonzalo

Use this link to cite:

http://hdl.handle.net/2183/27090

Exploiting Computation-Friendly Graph Compression Methods for Adjacency-Matrix Multiplication

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Ladra_Susana_2018_Graph_Compression_for_Matrix_Multiplication.pdf (125.29 KB)

Identifiers

URI: http://hdl.handle.net/2183/27090

DOI: 10.1109/DCC.2018.00039

Publication date

2018-03

Authors

Francisco, Alexandre P.

Gagie, Travis

Ladra, Susana

Navarro, Gonzalo

Bibliographic citation

A. Francisco, T. Gagie, S. Ladra and G. Navarro, "Exploiting Computation-Friendly Graph Compression Methods for Adjacency-Matrix Multiplication," 2018 Data Compression Conference, Snowbird, UT, USA, 2018, pp. 307-314, doi: 10.1109/DCC.2018.00039.

Abstract

[Abstract] Computing the product of the (binary) adjacency matrix of a large graph with a real-valued vector is an important operation that lies at the heart of various graph analysis tasks, such as computing PageRank. In this paper we show that some well-known Web and social graph compression formats are computation-friendly, in the sense that they allow boosting the computation. In particular, we show that the format of Boldi and Vigna allows computing the product in time proportional to the compressed graph size. Our experimental results show speedups of at least 2 on graphs that were compressed at least 5 times with respect to the original. We show that other successful graph compression formats enjoy this property as well.