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Open AccessArticle

A Measurement Model of Mutual Influence for Information Dissemination

College of Computer, National University of Defense Technology, Changsha 410073, China
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Authors to whom correspondence should be addressed.
Co-first authors of the article. Liang Zhang and Yong Quan contribute equally to the article.
Entropy 2020, 22(7), 725; https://doi.org/10.3390/e22070725
Received: 7 June 2020 / Revised: 26 June 2020 / Accepted: 27 June 2020 / Published: 30 June 2020
(This article belongs to the Section Information Theory, Probability and Statistics)
The recent development of the mobile Internet and the rise of social media have significantly enriched the way people access information. Accurate modeling of the probability of information propagation between users is essential for studying information dissemination issues in social networks. As the dissemination of information is inseparable from the interactions between users, the probability of propagation can be characterized by such interactions. In general, there are differences in the dissemination modes of information that carry different topics in a real social network. Using these factors, we propose a method (TMIVM) to measure the mutual influence between users at the topic level. The method associates two vectorization parameters for each user—an influence vector and a susceptibility vector—where the dimensions of the vector represent different topic categories. The magnitude of the mutual influence between users on different topics can be obtained by the product of the corresponding elements of the vectors. Specifically, in this article, we fit a social network historical information cascade data through Survival Analysis to learn the parameters of the influence and susceptibility vectors. The experimental results on a synthetic data set and a real Microblog data set show that this method better measures the propagation probability and information cascade predictions compared to other methods. View Full-Text
Keywords: mutual influence; Survival Analysis; information dissemination; propagation probability; TMIVM (Topic Mutual Influence Vector Model) mutual influence; Survival Analysis; information dissemination; propagation probability; TMIVM (Topic Mutual Influence Vector Model)
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Zhang, L.; Quan, Y.; Zhou, B.; Jia, Y.; Gao, L. A Measurement Model of Mutual Influence for Information Dissemination. Entropy 2020, 22, 725.

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