Compressing Graphs and Indexes with Recursive Graph Bisection

TL;DR

This paper introduces a recursive graph bisection method to improve the compression of large-scale graphs and inverted indexes, demonstrating significant compression gains and efficient parallel implementation.

Contribution

It extends a theoretical model for graph compression and presents a novel, simple, and scalable reordering algorithm based on recursive graph bisection.

Findings

Significant improvement in compression rates over existing heuristics

Efficient parallel and distributed implementation demonstrated on billion-scale graphs

The method is theoretically sound and practically effective

Abstract

Graph reordering is a powerful technique to increase the locality of the representations of graphs, which can be helpful in several applications. We study how the technique can be used to improve compression of graphs and inverted indexes. We extend the recent theoretical model of Chierichetti et al. (KDD 2009) for graph compression, and show how it can be employed for compression-friendly reordering of social networks and web graphs and for assigning document identifiers in inverted indexes. We design and implement a novel theoretically sound reordering algorithm that is based on recursive graph bisection. Our experiments show a significant improvement of the compression rate of graph and indexes over existing heuristics. The new method is relatively simple and allows efficient parallel and distributed implementations, which is demonstrated on graphs with billions of vertices and hundreds of billions of edges.