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.
翻译:为了在实践中管理大型图表,通常需要采用图形压缩,目的是减少存储和处理图形时使用的内存。文献中提出了高效压缩方法,特别是网络图表。在大多数情况下,这些方法与显著提高压缩率的顶端重新排序预处理步骤相结合。然而,这些方法在考虑其他类型的图表时效率不高。在本文中,我们侧重于双面图表的类别,通过提出双重重新排序方案,使脊椎重新排序阶段适应其特定结构。通过重新排序每组脊椎以尽量减少特定的分数,我们表明我们可以达到更好的压缩率。我们还建议,可以进一步完善这一方法,使节点的顺序更适应于订购阶段之后的压缩阶段。