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020 _a9783031794629
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024 7 _a10.1007/978-3-031-79462-9
_2doi
050 4 _aQA1-939
072 7 _aPB
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072 7 _aMAT000000
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082 0 4 _a510
_223
100 1 _aTang, Jie.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981852
245 1 0 _aSemantic Mining of Social Networks
_h[electronic resource] /
_cby Jie Tang, Juanzi Li.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXI, 193 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, 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.
650 0 _aMathematics.
_911584
650 0 _aInternet programming.
_935503
650 0 _aApplication software.
_981853
650 0 _aComputer networks .
_931572
650 0 _aOntology.
_95277
650 1 4 _aMathematics.
_911584
650 2 4 _aWeb Development.
_935505
650 2 4 _aComputer and Information Systems Applications.
_981854
650 2 4 _aComputer Communication Networks.
_981855
650 2 4 _aOntology.
_95277
700 1 _aLi, Juanzi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981856
710 2 _aSpringerLink (Online service)
_981857
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031794612
776 0 8 _iPrinted edition:
_z9783031794636
830 0 _aSynthesis Lectures on Data, Semantics, and Knowledge,
_x2691-2031
_981858
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79462-9
912 _aZDB-2-SXSC
942 _cEBK
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