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| Title | Graph Compression for Adjacency-Matrix Multiplication |
| Authors | Alexandre Francisco, Travis Gagie, Dominik Koppl, Susana Ladra, Gonzalo Navarro |
| Publication date | 2022 |
| 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 wellknown 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. |
| Downloaded | 9 times |
| Pages | article 193 |
| Volume | 3 |
| Journal name | SN Computer Science |
| Publisher | Springer (New York, NY, USA) |
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