Efficient distributed dynamic graph system

Research has focused on static large graph management. However, most of real-world networks evolve with time. Managing these evolving networks has attracted much attention in recent years. The networks' evolved data can be kept in a dynamic graph to improve the expressiveness and the quality of search queries as well as snapshot(s) retrieval. Storing the continuous evolution of the network in a dynamic graph makes its storage size grow. Existing dynamic graph models try to limit their storage by eliminating redundant data. However, their update time increases due to the elimination step. This illustrates that there is a tradeoff between the used storage and the update time. In this work, we address the problems of improving the update time of the networks' evolved data without increasing the storage redundancy as well as minimizing the needed memory storage. This paper merges the materialization technique with the distributed graph over servers. This merge reduces the update time and minimize the needed memory storage in an efficient manner as well as providing results with a better quality.