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Modeling Information Diffusion Online by Wang Haiyan (15 results)
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Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
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Modeling Information Diffusion in Online Social Networks with Partial Differential Equations (Surveys and Tutorials in the Applied Mathematical Sciences)
Book 8 of 9: Surveys and Tutorials in the Applied Mathematical SciencesSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 80.87
US$ 15.86 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Paperback. Condition: Brand New. 160 pages. 9.25x6.10x0.34 inches. In Stock.
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Modeling Information Diffusion in Online Social Networks with Partial Differential Equations
Book 8 of 9: Surveys and Tutorials in the Applied Mathematical SciencesPublished by Springer International Publishing, Springer International Publishing Mär 2020, 2020
ISBN 10: 3030388506 ISBN 13: 9783030388508
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 160 pp. Englisch.
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Modeling Information Diffusion in Online Social Networks with Partial Differential Equations
Book 8 of 9: Surveys and Tutorials in the Applied Mathematical SciencesPublished by Springer International Publishing, Springer International Publishing, 2020
ISBN 10: 3030388506 ISBN 13: 9783030388508
Language: English
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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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Taschenbuch. Condition: Neu. Modeling Information Diffusion in Online Social Networks with Partial Differential Equations | Haiyan Wang (u. a.) | Taschenbuch | xiii | Englisch | 2020 | Springer | EAN 9783030388508 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Modeling Information Diffusion in Online Social Networks with Partial Differential Equations
Book 8 of 9: Surveys and Tutorials in the Applied Mathematical SciencesPublished by Springer International Publishing Mrz 2020, 2020
ISBN 10: 3030388506 ISBN 13: 9783030388508
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 160 pp. Englisch.
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Modeling Information Diffusion in Online Social Networks with Partial Differential Equations
Book 8 of 9: Surveys and Tutorials in the Applied Mathematical SciencesPublished by Springer International Publishing, 2020
ISBN 10: 3030388506 ISBN 13: 9783030388508
Language: English
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a new and timely modeling approach for information diffusion in social mediaWritten by the experts who initiated the approach of modeling with partial differential equations (PDEs)Accessible to a wide range of readers in mat.
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