An effective graph summarization and compression technique for a large-scaled graph

摘要: Graphs are widely used in various applications, and their size is becoming larger over the passage of time. It necessary to reduce minimize main memory needs save storage space on disk. For these purposes, graph summarization compression approaches have been studied existing studies a large graph. Graph aggregates nodes having similar structural properties represent with reduced requirements. Whereas applies encoding techniques so that resultant lesser Considering usefulness both paradigms, we propose obtain best worlds by combining approaches. Hence, present greedy-based algorithm greatly reduces applying summarization. We also novel cost model for calculating ratio considering strategies. The uses proposed determine whether perform one or them every iteration. Through comprehensive experiments real-world datasets, show our achieves better than only up 16%.