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008			220601s2015 sz | o |||| 0|eng d
020			_a9783031794629
024	7		_a10.1007/978-3-031-79462-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D343 _b2015 EB
100	1		_aTang, Jie _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686947
245	1	0	_aSemantic Mining of Social Networks _cby Jie Tang, Juanzi Li
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XI, 193 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 Semantics and Knowledge _x2691-2031
505	0		_aAcknowledgments -- Introduction -- Social Tie Analysis -- Social Influence Analysis -- User Behavior Modeling and Prediction -- ArnetMiner: Deep Mining for Academic Social Networks -- Research Frontiers -- Bibliography -- Authors' Biographies .
520			_aOnline social networks have already become a bridge connecting our physical daily life with the (web-based) information space. This connection produces a huge volume of data, not only about the information itself, but also about user behavior. The ubiquity of the social Web and the wealth of social data offer us unprecedented opportunities for studying the interaction patterns among users so as to understand the dynamic mechanisms underlying different networks, something that was previously difficult to explore due to the lack of available data. In this book, we present the architecture of the research for social network mining, from a microscopic point of view. We focus on investigating several key issues in social networks. Specifically, we begin with analytics of social interactions between users. The first kinds of questions we try to answer are: What are the fundamental factors that form the different categories of social ties? How have reciprocal relationships been developed from parasocial relationships? How do connected users further form groups? Another theme addressed in this book is the study of social influence. Social influence occurs when one's opinions, emotions, or behaviors are affected by others, intentionally or unintentionally. Considerable research has been conducted to verify the existence of social influence in various networks. However, few literature studies address how to quantify the strength of influence between users from different aspects. In Chapter 4 and in [138], we have studied how to model and predict user behaviors. One fundamental problem is distinguishing the effects of different social factors such as social influence, homophily, and individual's characteristics. We introduce a probabilistic model to address this problem. Finally, we use an academic social network, ArnetMiner, as an example to demonstrate how we apply the introduced technologies for mining real social networks. In this system, we try to mine knowledge from both the informative (publication) network and the social (collaboration) network, and to understand the interaction mechanisms between the two networks. The system has been in operation since 2006 and has already attracted millions of users from more than 220 countries/regions.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9163805 _aWeb semántica
700	1		_aLi, Juanzi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687722
776	0	8	_iPrinted edition: _z9783031794612
776	0	8	_iPrinted edition: _z9783031794636
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79462-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eb _zSI