000			04899nam a22005175i 4500
001			978-3-031-01850-3
003			DE-He213
005			20240730163437.0
007			cr nn 008mamaa
008			220601s2014 sz | s |||| 0|eng d
020			_a9783031018503 _9978-3-031-01850-3
024	7		_a10.1007/978-3-031-01850-3 _2doi
050		4	_aTK5105.5-5105.9
072		7	_aUKN _2bicssc
072		7	_aCOM043000 _2bisacsh
072		7	_aUKN _2thema
082	0	4	_a004.6 _223
100	1		_aChen, Wei. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978556
245	1	0	_aInformation and Influence Propagation in Social Networks _h[electronic resource] / _cby Wei Chen, Carlos Castillo, Laks V.S. Lakshmanan.
250			_a1st ed. 2014.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014.
300			_aXV, 161 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_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.
650		0	_aComputer networks . _931572
650		0	_aData structures (Computer science). _98188
650		0	_aInformation theory. _914256
650	1	4	_aComputer Communication Networks. _978557
650	2	4	_aData Structures and Information Theory. _931923
700	1		_aCastillo, Carlos. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978558
700	1		_aLakshmanan, Laks V.S. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978559
710	2		_aSpringerLink (Online service) _978560
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031007224
776	0	8	_iPrinted edition: _z9783031029783
830		0	_aSynthesis Lectures on Data Management, _x2153-5426 _978561
856	4	0	_uhttps://doi.org/10.1007/978-3-031-01850-3
912			_aZDB-2-SXSC
942			_cEBK
999			_c84610 _d84610