Probabilistic solutions of influence propagation on social networks

Given fixed budgets, companies attempt to obtain maximum coverage on a social network by targeting at influential individuals. This viral marketing is often modeled by the independent cascade model. However, identifying the most influential people by computing influence spread is NP-hard, and various approximate algorithms are developed. In this paper, we emphasize the probabilistic nature of influence propagation. We propose to use exact probabilistic solutions and prove an inclusion-exclusion principle for computing influence spread. Our probabilistic solutions can significantly speed up the computation of influence spread. We also give a probabilistic-additive incremental search strategy to solve the influence maximization problem, i.e., to find a subset of individuals that has the largest influence spread in the end. Experiments on real data sets demonstrated the effectiveness and efficiency of our methods.