Compressing bipartite graphs with a dual reordering scheme

TL;DR

This paper introduces a dual reordering scheme tailored for bipartite graphs that improves compression efficiency by optimizing vertex orderings based on their specific structure.

Contribution

It proposes a novel dual reordering method for bipartite graphs, enhancing compression rates compared to existing vertex reordering techniques.

Findings

Achieves better compression rates for bipartite graphs

Reordering each vertex group minimizes a specific score

Potential for further refinement to optimize compression

Abstract

In order to manage massive graphs in practice, it is often necessary to resort to graph compression, which aims at reducing the memory used when storing and processing the graph. Efficient compression methods have been proposed in the literature, especially for web graphs. In most cases, they are combined with a vertex reordering pre-processing step which significantly improves the compression rate. However, these techniques are not as efficient when considering other kinds of graphs. In this paper, we focus on the class of bipartite graphs and adapt the vertex reordering phase to their specific structure by proposing a dual reordering scheme. By reordering each group of vertices in the purpose of minimizing a specific score, we show that we can reach better compression rates. We also suggest that this approach can be further refined to make the node orderings more adapted to the compression phase that follows the ordering phase.