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Focusing on the intersection of mathematics and social media, this book introduces a dynamic modeling approach using partial differential equations to analyze information diffusion in online networks. It employs the Laplacian matrix to identify user communities, embedding them in Euclidean space for further analysis. The authors validate their models with Twitter data, exploring significant events like the Egyptian revolution and predicting influenza prevalence. This innovative method proposes a paradigm shift in understanding information flow, offering a foundation for future spatio-temporal modeling in the big-data era.
Buchkauf
Modeling Information Diffusion in Online Social Networks with Partial Differential Equations, Haiyan Wang, Feng Wang, Kuai Xu
- Sprache
- Erscheinungsdatum
- 2020
- product-detail.submit-box.info.binding
- (Paperback)
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- Titel
- Modeling Information Diffusion in Online Social Networks with Partial Differential Equations
- Sprache
- Englisch
- Autor*innen
- Haiyan Wang, Feng Wang, Kuai Xu
- Erscheinungsdatum
- 2020
- Einband
- Paperback
- Seitenzahl
- 160
- ISBN13
- 9783030388508
- Reihe
- Schlagwörter
- Sachbücher, Sozialwissenschaften, Wissenschaft & Mathematik, Mathematik
- Beschreibung
- Focusing on the intersection of mathematics and social media, this book introduces a dynamic modeling approach using partial differential equations to analyze information diffusion in online networks. It employs the Laplacian matrix to identify user communities, embedding them in Euclidean space for further analysis. The authors validate their models with Twitter data, exploring significant events like the Egyptian revolution and predicting influenza prevalence. This innovative method proposes a paradigm shift in understanding information flow, offering a foundation for future spatio-temporal modeling in the big-data era.