Large Graphs Compression using a combined algorithm

Web Graph provides a framework for large-scale graph compression; it is the foundation of many real-world datasets. The increasing size of graphs presents a major obstacle in exploiting modern compression techniques. Recently some techniques have proved sufficiently efficient in allowing greater storage of Web graphs in a limited memory. These techniques mainly use repetitions that exist in the graphs for compressing purposes. In this paper I use similarity property technique based on the pages that are proximal in the lexicographic ordering which tend to have similar sets of neighbors. This technique together with an optimized Greedy algorithm can produce a new combinational algorithm that can be used to compress Web graph more efficiently

کلیدواژه ها:

Web graph ، compression ، similarity ، Greedy algorithm