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Influence maximization (im) is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. Despite the progress achieved by state-of-the-art greedy im techniques, they suffer from two key limitations. Firstly, they are inefficient as they can take days to find seeds in very large real-world networks. Secondly, although extensive research in social psychology suggests that humans will readily conform to the wishes or beliefs of others, surprisingly, existing im techniques are conformity-unaware. That is, they only utilize an individual’s ability to influence another but ignores conformity (a person’s inclination to be influenced) of the individuals. In this paper, we propose a novel conformity-aware cascade () model which leverages on the interplay between influence and conformity in obtaining the influence probabilities of nodes from underlying data for …