Community Aware Influence Maximization on Large Scale Networks Using Mapreduce

Influence maximization problem is a well known problem to find the top-k seed users who can maximize the spread of information in a social network. The primary concern is monte carlo simulations method is suffering with scalability issues while the selection of seed users .It takes days to find potential seed users in large datasets. In this paper, we propose a highly scalable algorithm for identifying Influential nodes on largescale graph using the Map Reduce framework. We perform a combined community detection algorithm and consider the most influential users in the community as the candidate for the top-k seeds. This approximation allows us to divide the whole graph into multiple sub graphs that can be processed independently. Then, for each sub graph, a Map Reduce based algorithm is designed to identify the minimum-sized influential vertices for the whole graph. This original approach contrasts with previous influence propagation models, which did not use similarity opportunities among members of communities to maximize influence propagation. The performance results show that the model activates a higher number of overall nodes in contemporary social networks, as compared to existing landmark approaches.