Abstract
In a social network, many social behaviors exhibit the property of association that the behaviors of an individual influence the others who are contacted. For example, the message that carries the news may spread through a portion of the social network that constructs a social interaction propagation graph. In this paper, we model the social interaction influence and its propagation over the social network. We compute the infection probability in the social interaction propagation graph. Furthermore, we implement the Apriori algorithm that allows us to explore the social interaction association and compute the infection probability on a large scale social network. The complexity is compared with a centralized implementation and a distributed implementation.
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