000			04110nam a22005175i 4500
001			978-3-031-01900-5
003			DE-He213
005			20240730163444.0
007			cr nn 008mamaa
008			220601s2010 sz | s |||| 0|eng d
020			_a9783031019005 _9978-3-031-01900-5
024	7		_a10.1007/978-3-031-01900-5 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
072		7	_aUNF _2thema
072		7	_aUYQE _2thema
082	0	4	_a006.312 _223
100	1		_aTang, Lei. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978635
245	1	0	_aCommunity detection and mining in social media _h[electronic resource] / _cby Lei Tang, Huan Liu.
250			_a1st ed. 2010.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2010.
300			_aX, 96 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 Mining and Knowledge Discovery, _x2151-0075
505	0		_aSocial Media and Social Computing -- Nodes, Ties, and Influence -- Community Detection and Evaluation -- Communities in Heterogeneous Networks -- Social Media Mining.
520			_aThe past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information. Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining.
650		0	_aData mining. _93907
650		0	_aStatistics . _931616
650	1	4	_aData Mining and Knowledge Discovery. _978636
650	2	4	_aStatistics. _914134
700	1		_aLiu, Huan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978637
710	2		_aSpringerLink (Online service) _978638
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031007729
776	0	8	_iPrinted edition: _z9783031030284
830		0	_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 _978639
856	4	0	_uhttps://doi.org/10.1007/978-3-031-01900-5
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942			_cEBK
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