Analysis of influence maximization in large-scale social networks

Influence maximization is an important problem in online social networks. With the scale of social networks increasing, the requirements of solutions for influence maximization are becoming more and more strict. In this paper, we discuss two basic methods to compute the influence in general social networks, and then reveal that the computation of influence in series-parallel graph is in linear time complexity. Finally, we propose an novel method to solve influence maximization and show that it has a good performance.

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