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 net-works 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 demon-strated on graphs with billions of vertices and hundreds of billions of edges.

Filed under: Big Data | Large Scale Machine Learning Systems | Graph Mining and Social Networks