A Markov Chain Approach to big data ranking systems

In this paper, we propose a Markov Chain Approach to big data ranking systems. In doing so first, we create a transition matrix which will store a calculated score between every applicable pair of nodes on a large scale network. We then compute the stationary distribution of the Markov Chain with the transition matrix and rank the outcomes of this matrix to get the most influential users on the network. We have implemented our algorithm on the neo4j platform and tested it with some sample datasets. The experimental results show that the proposed method is promising for mining influential nodes in big social networks.