| 000 | 03690nam a22005655i 4500 | ||
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| 001 | 978-981-97-0421-7 | ||
| 003 | DE-He213 | ||
| 005 | 20251030112015.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240427s2024 si | s |||| 0|eng d | ||
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_a9789819704217 _9978-981-97-0421-7 |
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_a10.1007/978-981-97-0421-7 _2doi |
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| 100 | 1 |
_aDong, Yucheng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _975913 |
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| 245 | 1 | 0 |
_aSocial Network DeGroot Model _h[electronic resource] : _bSupporting Consensus Reaching in Opinion Dynamics / _cby Yucheng Dong, Zhaogang Ding, Gang Kou. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024. |
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| 300 |
_aXII, 166 p. 64 illus., 38 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 505 | 0 | _aChapter 1. Introduction -- Chapter 2. Social Network DeGroot Model: Consensus and convergence speed -- Chapter 3. Consensus reaching through adding minimum interactions -- Chapter 4. Strategic Manipulations with trust relationships -- Chapter 5. Risk control in the evolution of public opinions -- Chapter 6. Social Network DeGroot Model in uncertain contexts. | |
| 520 | _aThis book investigates the DeGroot model in social network contexts, and proposes the social network DeGroot (SNDG) model. Specifically, this book focuses on two core research problems in the SNDG model: (i) Social network structures to reach a stable state (consensus, polarization, or fragmentation); and (ii) the convergence rate to reach a stable state. Furthermore, the authors generalize the SNDG model in an uncertain context, showing the effects of interval opinions on the SNDG model. In this book, the authors also discuss the applications of the SNDG model to support group decision making, including consensus reaching through adding minimum interactions, trust relationships manipulations, and risk control issues in the social network. Apart from theoretical analysis, detailed experimental simulations with real and random data will be applied to validate our research. This book is the first to connect opinion dynamics, social network and group decision making. The results reported can help us understand the evolution of public opinions in social network contexts and provide new tools to support consensus reaching in group decision making. | ||
| 650 | 0 |
_aCommunication in organizations. _960034 |
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| 650 | 0 |
_aSocial sciences _xNetwork analysis. _975914 |
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| 650 | 0 |
_aSocial psychology. _959087 |
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| 650 | 1 | 4 |
_aOrganizational and Strategic Communication. _968164 |
| 650 | 2 | 4 |
_aNetwork Research. _975915 |
| 650 | 2 | 4 |
_aSocial Psychology. _959087 |
| 700 | 1 |
_aDing, Zhaogang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _975916 |
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| 700 | 1 |
_aKou, Gang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _975917 |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
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_iPrinted edition: _z9789819704200 |
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_iPrinted edition: _z9789819704224 |
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_iPrinted edition: _z9789819704231 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-97-0421-7 |
| 912 | _aZDB-2-BUM | ||
| 912 | _aZDB-2-SXBM | ||
| 999 |
_c59454 _d59454 |
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