LEADER 03908nam 22005895 450 001 9910483165503321 005 20200706091701.0 010 $a3-030-38852-2 024 7 $a10.1007/978-3-030-38852-2 035 $a(CKB)4100000010672605 035 $a(DE-He213)978-3-030-38852-2 035 $a(MiAaPQ)EBC6138232 035 $a(PPN)243226306 035 $a(EXLCZ)994100000010672605 100 $a20200316d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModeling Information Diffusion in Online Social Networks with Partial Differential Equations /$fby Haiyan Wang, Feng Wang, Kuai Xu 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIII, 144 p. 39 illus., 29 illus. in color.) 225 1 $aSurveys and Tutorials in the Applied Mathematical Sciences,$x2199-4765 ;$v7 311 $a3-030-38850-6 327 $aOrdinary 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. 330 $aThe book lies at the interface of mathematics, social media analysis, and data science. Its authors aim to introduce a new dynamic modeling approach to the use of partial differential equations for describing information diffusion over online social networks. The eigenvalues and eigenvectors of the Laplacian matrix for the underlying social network are used to find communities (clusters) of online users. Once these clusters are embedded in a Euclidean space, the mathematical models, which are reaction-diffusion equations, are developed based on intuitive social distances between clusters within the Euclidean space. The models are validated with data from major social media such as Twitter. In addition, mathematical analysis of these models is applied, revealing insights into information flow on social media. Two applications with geocoded Twitter data are included in the book: one describing the social movement in Twitter during the Egyptian revolution in 2011 and another predicting influenza prevalence. The new approach advocates a paradigm shift for modeling information diffusion in online social networks and lays the theoretical groundwork for many spatio-temporal modeling problems in the big-data era. 410 0$aSurveys and Tutorials in the Applied Mathematical Sciences,$x2199-4765 ;$v7 606 $aPartial differential equations 606 $aApplication software 606 $aCommunication 606 $aPartial Differential Equations$3https://scigraph.springernature.com/ontologies/product-market-codes/M12155 606 $aComputer Appl. in Social and Behavioral Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/I23028 606 $aCommunication Studies$3https://scigraph.springernature.com/ontologies/product-market-codes/X28000 615 0$aPartial differential equations. 615 0$aApplication software. 615 0$aCommunication. 615 14$aPartial Differential Equations. 615 24$aComputer Appl. in Social and Behavioral Sciences. 615 24$aCommunication Studies. 676 $a515.353 700 $aWang$b Haiyan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0899993 702 $aWang$b Feng$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aXu$b Kuai$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483165503321 996 $aModeling Information Diffusion in Online Social Networks with Partial Differential Equations$92368775 997 $aUNINA