Modeling information diffusion in online social networks with partial differential equations

• 作者： Wang, Haiyan, author.
• 其他作者：
  • Wang, Feng, author.
  • Xu, Kuai, author.
  • SpringerLink (Online service)
• 其他題名：
  • Surveys and tutorials in the applied mathematical sciences ;
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Surveys and tutorials in the applied mathematical sciences,volume 7
• 主題： Differential equations, Partial. , Diffusion--Mathematical models. , Online social networks. , Partial Differential Equations. , Computer Appl. in Social and Behavioral Sciences. , Communication Studies.
• ISBN： 9783030388522 (electronic bk.) 、 9783030388508 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Ordinary Differential Equation Models on Social Networks -- Spatio-temporal Patterns of Information Diffusion -- Clustering of Online Social Network Graphs -- Partial Differential Equation Models -- Modeling Complex Interactions -- Mathematical Analysis -- Applications.
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• 摘要註: The book lies at the interface of mathematics, social media analysis, and data science. Its authors aim to introduce a new dynamic modeling approach to the use of partial differential equations for describing information diffusion over online social networks. The eigenvalues and eigenvectors of the Laplacian matrix for the underlying social network are used to find communities (clusters) of online users. Once these clusters are embedded in a Euclidean space, the mathematical models, which are reaction-diffusion equations, are developed based on intuitive social distances between clusters within the Euclidean space. The models are validated with data from major social media such as Twitter. In addition, mathematical analysis of these models is applied, revealing insights into information flow on social media. Two applications with geocoded Twitter data are included in the book: one describing the social movement in Twitter during the Egyptian revolution in 2011 and another predicting influenza prevalence. The new approach advocates a paradigm shift for modeling information diffusion in online social networks and lays the theoretical groundwork for many spatio-temporal modeling problems in the big-data era.
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