Exploiting Computation-Friendly Graph Compression Methods

TL;DR

This paper demonstrates that certain graph compression formats enable faster matrix-vector multiplication, significantly improving computation speed for large graphs in tasks like PageRank.

Contribution

It reveals that well-known web and social graph compression formats are computation-friendly, allowing efficient matrix operations proportional to compressed size.

Findings

Boldi and Vigna's format enables proportional computation time

Speedups of at least 2x on highly compressed graphs

Other compression formats also support efficient computation

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.