Modeling Information Diffusion in Online Social Networks with Partial Differential Equations (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Surveys and Tutorials in the Applied Mathematical Sciences, 7
Release date:	March 2020
First published:	2020
Authors:	Haiyan Wang • Feng Wang • Kuai Xu
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	144
Edition:	1st ed. 2020
ISBN-13:	978-3-03-038850-8
Categories:	Books > Reference & Interdisciplinary > Communication studies > General Books > Science & Mathematics > Mathematics > Calculus & mathematical analysis > Differential equations Promotions
LSN:	3-03-038850-6
Barcode:	9783030388508

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