Common Neighbor Query-Friendly Triangulation-Based Large-Scale Graph Compression

摘要

Large-scale graphs appeax in many web applications, and are inevitable in web data management and mining. A lossless compression method for large-scale graphs, named as bound-triangulation, is introduced in this paper. It differs itself from other graph compression methods in that: 1) it can achieve both good compression ratio and low compression time. 2) The compression ratio can be controlled by users, so that compression ratio and processing performance can be balanced. 3) It supports efficient common neighbor query processing over compressed graphs. Thus, it can support a wide range of graph processing tasks. Empirical study over two real-life large-scale social networks, which different underlying data distributions, show the superior of the proposed method over other existing graph compression methods.