Abstract
Social media has become very popular nowadays by spreading plentiful human-centric data to a large number of audience. A rich body of literature has studied the influence maximization problem in social networks under certain propagation models. However, existing models suffer from the assumption of unlimited propagation time and some of them are nondeterministic. Influence maximization algorithms within those frameworks are often trading off between performance guarantee and computational cost. In this paper, we try to formulate the influence maximization problem in another way, where we limit the propagation time to a predefined propagation round and in each round maintain the probability to make the propagation process more tractable. We introduce a new diffusion model called a probability propagation model and formulate this optimization problem as a holistic probability maximization problem. We show that information diffusion estimation in the proposed framework is not NP-hard. Hence, any algorithm within it will be more efficient. However, the maximization problem is still NP-hard. After proving the submodularity in the proposed framework, we design a partial-updating greedy algorithm and its heuristic extension to solve the maximization problem. Extensive experiments on four synthetic data sets and four real-world data sets from Facebook, Wikipedia, arXiv, and Epinions demonstrate the effectiveness of the proposed algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.