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008			220601s2014 sz | o |||| 0|eng d
020			_a9783031018503
024	7		_a10.1007/978-3-031-01850-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHM742 _b2014 EB
100	1		_aChen, Wei _d1968- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _963801
245	1	0	_aInformation and Influence Propagation in Social Networks _cby Wei Chen, Carlos Castillo, Laks V. S. Lakshmanan
250			_a1st edition 2014
264		1	_aCham _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XV, 161 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Management _x2153-5426
505	0		_aAcknowledgments -- Introduction -- Stochastic Diffusion Models -- Influence Maximization -- Extensions to Diffusion Modeling and Influence Maximization -- Learning Propagation Models -- Data and Software for Information/Influence: Propagation Research -- Conclusion and Challenges -- Bibliography -- Authors' Biographies -- Index.
520			_aResearch on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications.
988			_aSynthesis Collection of Technology_2014
650		7	_2embne _9431622 _aRedes sociales en Internet
700	1		_aCastillo, Carlos _d1977- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686315
700	1		_aLakshmanan, Laks V. S. _d1959- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686316
776	0	8	_iPrinted edition: _z9783031007224
776	0	8	_iPrinted edition: _z9783031029783
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01850-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI