Predicting information diffusion probabilities in social networks: A Bayesian networks based approach
作者：
Highlights：
• Proposed approach predicts information diffusion probability in the social networks.
• A machine learning based Bayesian network model is trained and tested.
• Method utilizes diffusion history, network and content based features for modeling.
• Method utilizes LDA topic model to formulate user interests and message semantics.
• Experiments are performed on Twitter data to show the effectiveness of the method.
摘要
•Proposed approach predicts information diffusion probability in the social networks.•A machine learning based Bayesian network model is trained and tested.•Method utilizes diffusion history, network and content based features for modeling.•Method utilizes LDA topic model to formulate user interests and message semantics.•Experiments are performed on Twitter data to show the effectiveness of the method.
论文关键词：Social network analysis,Information diffusion,Diffusion network,Bayesian network modeling,Diffusion probability
论文评审过程：Received 28 April 2017, Revised 28 June 2017, Accepted 1 July 2017, Available online 3 July 2017, Version of Record 4 September 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2017.07.003
