Use this link to cite:
http://hdl.handle.net/2183/31180 Graph Compression for Adjacency-Matrix Multiplication
Loading...
Identifiers
Publication date
Authors
Advisors
Other responsabilities
Journal Title
Bibliographic citation
Francisco, A.P., Gagie, T., Köppl, D. et al. Graph Compression for Adjacency-Matrix Multiplication. SN COMPUT. SCI. 3, 193 (2022). https://doi.org/10.1007/s42979-022-01084-2
Type of academic work
Academic degree
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 webgraph and social graph compression formats are computation-friendly, in the sense that they allow boosting the computation. We focus on the compressed representations of (a) Boldi and Vigna and (b) Hernández and Navarro, and show that the product computation can be conducted 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.
Description
19 April 2022
A Correction to this paper has been published: https://doi.org/10.1007/s42979-022-01141-w
Editor version
Rights
Atribución 3.0 España
Collections
Except where otherwise noted, this item's license is described as Atribución 3.0 España