A Probability PPV Model for Social Network Influence Maximization Problem

For Influence Maximization(IM) problem based on social network, effective and personalized probability learning method was still not theoretical guaranteed. In this paper, we proposed a PPV probability model based on IM problem, which effectively learnt influence probabilities and personal-ized influence for each node pair. By clustering user groups and analyzing similarity of users from both offline action log and online social network topological structure, we estimated reliable parameters of our probability model, differed user's influence with different features on its neighbors, and improved probability learning procedure. PageRank algorithm and fuzzy cognitive map concept help to validate our model in PPV calculation. Experiments show that our approach outperforms the state-of-the-art algorithm. As preference property  and transition property p , which is called PPV in our model, explains features of influence between user pairs, accuracy of personalized influence probability is improved. Keywords-influence maximization; viral marketing; PageRank algorithm; personalization

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