Abstract
The World Wide Web structure can be represented by a directed graph named as the web graph. The web graphs have been used in a wide range of applications. However, the increasingly large-scale web graphs pose great challenges to the traditional memory-resident graph algorithms. In the literature, K 2-tree can efficiently compress the web graphs while supporting fast querying in the compressed data. Inspired by K 2-tree, we propose the Delta-K 2-tree compression approach, which exploits the characteristics of similarity between neighbor nodes in the web graphs. In addition, we design a node reordering algorithm to further improve the compression ratio. We compare our approach with the state-of-the-art algorithms, including K 2-tree, WebGraph, and AD. Experimental results of web graph compression on four datasets show that our Delta-K 2-tree approach outperforms the other three in compression ratio (1.66-2.55 bits per link), and meanwhile supports fast forward and reverse querying in graphs.
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