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  1. Bog
  2. Regning og matematisk analyse
  3. Differentialregning & ligninger

Modeling Information Diffusion in Online Social Networks with Partial Differential Equations

Af: Feng Wang, Haiyan Wang, Kuai Xu Engelsk Paperback

Modeling Information Diffusion in Online Social Networks with Partial Differential Equations

Af: Feng Wang, Haiyan Wang, Kuai Xu Engelsk Paperback
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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, 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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 144
ISBN-13: 9783030388508
Indbinding: Paperback
Udgave:
ISBN-10: 3030388506
Kategori: Differentialregning & ligninger
Udg. Dato: 17 mar 2020
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 17 mar 2020
Forfatter(e): Feng Wang, Haiyan Wang, Kuai Xu
Forfatter(e) Feng Wang, Haiyan Wang, Kuai Xu

Kategori Differentialregning & ligninger

ISBN-13 9783030388508

Sprog Engelsk

Indbinding Paperback

Sider 144

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 17 mar 2020

Oplagsdato 17 mar 2020

Forlag Springer Nature Switzerland AG

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