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Record Nr. |
UNINA9910483165503321 |
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Autore |
Wang Haiyan |
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Titolo |
Modeling Information Diffusion in Online Social Networks with Partial Differential Equations / / by Haiyan Wang, Feng Wang, Kuai Xu |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XIII, 144 p. 39 illus., 29 illus. in color.) |
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Collana |
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Surveys and Tutorials in the Applied Mathematical Sciences, , 2199-4765 ; ; 7 |
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Disciplina |
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Soggetti |
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Partial differential equations |
Application software |
Communication |
Partial Differential Equations |
Computer Appl. in Social and Behavioral Sciences |
Communication Studies |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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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Sommario/riassunto |
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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, |
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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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