000			02244nam a22003135i 4500
001			91675
005			20240506155207.0
010			_a978-3-030-38852-2 _dcompra
090			_a91675
100			_a20231023d2020 k||y0pory50 ba
101	0		_aeng
102			_aCH _bCham
200	1		_aModeling information diffusion in online social networks with partial differential equations _bDocumento eletrónico _fHaiyan Wang, Feng Wang, Kuai Xu
210			_aCham _cSpringer International Publishing _d2020
215			_aXIII, 144 p. _cil.
225	2		_aSurveys and Tutorials in the Applied Mathematical Sciences _vvol. 7
303			_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.
606			_91072 _aEquações diferenciais
606			_96332 _aModelos matemáticos
680			_aQA371
700			_972883 _aWang _bHaiyan
701			_972884 _aWang _bFeng _4070
701			_972885 _aXu _bKuai _4070
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-3-030-38852-2
942			_2lcc _cF _n0