| 000 | 07707nam a2200949 i 4500 | ||
|---|---|---|---|
| 001 | 7110053 | ||
| 003 | IEEE | ||
| 005 | 20200413152917.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 150520s2015 caua foab 000 0 eng d | ||
| 020 |
_a9781608458585 _qebook |
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| 020 |
_z9781608458578 _qprint |
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| 024 | 7 |
_a10.2200/S00629ED1V01Y201502WBE011 _2doi |
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| 035 | _a(CaBNVSL)swl00405028 | ||
| 035 | _a(OCoLC)909652748 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aHM742 _b.T257 2015 |
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| 082 | 0 | 4 |
_a302.30285 _223 |
| 100 | 1 |
_aTang, Jie _c(Computer scientist), _eauthor. |
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| 245 | 1 | 0 |
_aSemantic mining of social networks / _cJie Tang and Juanzi Li. |
| 264 | 1 |
_aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _c2015. |
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| 300 |
_a1 PDF (xi, 193 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on the semantic web, theory and technology, _x2160-472X ; _v# 11 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 177-191). | ||
| 505 | 0 | _a1. Introduction -- 1.1 Background -- 1.1.1 Social theories -- 1.1.2 Social tie analysis -- 1.1.3 Social influence analysis -- 1.1.4 User modeling and actions -- 1.1.5 Graphical models -- 1.2 Book outline -- | |
| 505 | 8 | _a2. Social tie analysis -- 2.1 Overview -- 2.2 Predicting missing links -- 2.2.1 Similarity metrics -- 2.2.2 Matrix factorization -- 2.3 Inferring social ties -- 2.3.1 Problem formulation -- 2.3.2 Unsupervised learning to infer social ties -- 2.3.3 Supervised learning to infer social ties -- 2.3.4 Actively learning to infer social ties -- 2.3.5 Inferring social ties across heterogeneous networks -- 2.4 Conclusions -- | |
| 505 | 8 | _a3. Social influence analysis -- 3.1 Overview -- 3.2 Mining topic-level social influence analysis -- 3.2.1 Topical affinity propagation -- 3.2.2 Dynamic social influence analysis -- 3.2.3 Model application -- 3.2.4 Experimental results -- 3.2.5 Summary -- 3.3 Mining topic-level influence from heterogeneous networks -- 3.3.1 The approach framework -- 3.3.2 Influence propagation and aggregation -- 3.3.3 Conservative and non-conservative propagation -- 3.3.4 User behavior prediction -- 3.3.5 Evaluation -- 3.3.6 Summary -- 3.4 Conclusions -- | |
| 505 | 8 | _a4. User behavior modeling and prediction -- 4.1 Overview -- 4.2 Approach framework for social action prediction -- 4.2.1 Model learning -- 4.3 Evaluation -- 4.3.1 Evaluation metrics -- 4.3.2 Prediction performance -- 4.3.3 Efficiency performance -- 4.3.4 Qualitative case study -- 4.4 Summary -- | |
| 505 | 8 | _a5. ArnetMiner: deep mining for academic social networks -- 5.1 Overview -- 5.2 Researcher profile extraction -- 5.2.1 A unified approach to profiling -- 5.2.2 Profile extraction performance -- 5.3 Name disambiguation -- 5.3.1 A unified probabilistic framework -- 5.3.2 Name disambiguation performance -- 5.4 Topic modeling -- 5.4.1 Our proposed topic models -- 5.5 Expertise search -- 5.5.1 Data sets and evaluation measures -- 5.5.2 Results -- 5.6 Academic social network mining -- 5.6.1 Mining advisor-advisee relationships -- 5.6.2 Measuring academic influence -- 5.6.3 Modeling researcher interests -- 5.7 Conclusions -- | |
| 505 | 8 | _a6. Research frontiers -- A. Resources -- Software -- Data sets -- Bibliography -- Authors' biographies. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on May 20, 2015). | ||
| 650 | 0 | _aOnline social networks. | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aSemantic Web. | |
| 653 | _asocial tie | ||
| 653 | _astrong/weak ties | ||
| 653 | _aparasocial interactions | ||
| 653 | _areciprocity | ||
| 653 | _asocial influence | ||
| 653 | _acollective classification | ||
| 653 | _agraphical model | ||
| 653 | _asocial network analysis | ||
| 653 | _asocial relationship | ||
| 653 | _arelationship mining | ||
| 653 | _alink prediction | ||
| 653 | _ainfluence maximization | ||
| 653 | _anetwork centrality | ||
| 653 | _auser modeling | ||
| 653 | _asocial action | ||
| 653 | _asocial theories | ||
| 653 | _asocial balance | ||
| 653 | _asocial status | ||
| 653 | _atriadic closure | ||
| 653 | _afactor graph | ||
| 653 | _ainfluence propagation | ||
| 653 | _aconservative influence propagation | ||
| 653 | _anonconservative influence propagation | ||
| 653 | _auser behaviour prediction | ||
| 653 | _aprofile extraction | ||
| 653 | _aexpert finding | ||
| 653 | _aname disambiguation | ||
| 653 | _aArnetMiner | ||
| 700 | 1 |
_aLi, Juanzi., _eauthor. |
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| 776 | 0 | 8 |
_iPrint version: _z9781608458578 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on the semantic web, theory and technology ; _v# 11. _x2160-472X |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7110053 |
| 999 |
_c562132 _d562132 |
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