Probabilistic solutions of influence propagation on social networks

Authors:
Miao Zhang;Chunni Dai;Chris Ding;Enhong Chen
Affiliations:
University of Texas at Arlington, Arlington, TX, USA;Shanghai Jianqiao College, Shanghai, China;University of Texas at Arlington, Arlington, TX, USA;University of Science and Technology of China, Hefei, China
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

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.