用递归图二分压缩图和索引
Compressing Graphs and Indexes with Recursive Graph Bisection
摘要：
图重排是一种增强图表示局部性的强大技术，在许多应用中有帮助。我们研究如何使用该技术来提高压缩的图形和倒排索引。我们扩展了Chierichetti等人（KDD 2009）的最新理论模型，用于图压缩，并展示了如何将其用于社交网络和Web图的压缩友好重新排序，以及如何在倒排索引中分配文档标识符。我们设计并实现了一个新的理论上健全的重新排序算法，是基于递归图二分法。实验结果表明，该算法在图和索引的压缩率上比现有算法有了显著的提高。新方法相对简单，并允许高效的并行和分布式实现，这是在具有数十亿个顶点和数千亿条边的图上证明的。
英文摘要：
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.
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