Big data is everywhere. One increasingly important type of big data is in the form of networks— gene regulatory networks, brain networks, health/disease networks, and online social networks like Facebook, Google+, and Twitter to name a few. Often massive networks are presented in the form of a big graph consists of a set of vertices and a set of edges. For example, in a social network, friend connections or wall posts form edges of the network; in a health network monitoring the spread of a disease in the population, edges representing contaminations might be added to the network. Most of these networks are huge and so, the resulting data becomes too large to store in the computer’s memory. Therefore, we require algorithms to parallelize the data on a big number of machines having moderate memory by using few communication rounds