| 000 | 07170nam a2200829 i 4500 | ||
|---|---|---|---|
| 001 | 6812846 | ||
| 003 | IEEE | ||
| 005 | 20200413152912.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 131113s2014 caua foab 001 0 eng d | ||
| 020 |
_a9781627051163 _qebook |
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| 020 |
_z9781627051156 _qpaperback |
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| 024 | 7 |
_a10.2200/S00527ED1V01Y201308DTM037 _2doi |
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| 035 | _a(CaBNVSL)swl00402944 | ||
| 035 | _a(OCoLC)862937428 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aHM742 _b.C445 2014 |
|
| 082 | 0 | 4 |
_a006.754 _223 |
| 090 |
_a _bMoCl _e201308DTM037 |
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| 100 | 1 |
_aChen, Wei., _eauthor. |
|
| 245 | 1 | 0 |
_aInformation and influence propagation in social networks / _cWei Chen, Laks V.S. Lakshmanan, Carlos Castillo. |
| 264 | 1 |
_aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _c2014. |
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| 300 |
_a1 PDF (xv, 161 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 data management, _x2153-5426 ; _v# 37 |
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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. | ||
| 500 | _aSeries from website. | ||
| 504 | _aIncludes bibliographical references (pages 143-155) and index. | ||
| 505 | 0 | _a1. Introduction -- 1.1 Social networks and social influence -- 1.1.1 Examples of social networks -- 1.1.2 Examples of information propagation -- 1.2 Social influence examples -- 1.3 Social influence analysis applications -- 1.4 The flip side -- 1.5 Outline of this book -- | |
| 505 | 8 | _a2. Stochastic diffusion models -- 2.1 Main progressive models -- 2.1.1 Independent cascade model -- 2.1.2 Linear threshold model -- 2.1.3 Submodularity and monotonicity of influence spread function -- 2.1.4 General threshold model and general cascade model -- 2.2 Other related models -- 2.2.1 Epidemic models -- 2.2.2 Voter model -- 2.2.3 Markov random field model -- 2.2.4 Percolation theory -- | |
| 505 | 8 | _a3. Influence maximization -- 3.1 Complexity of influence maximization -- 3.2 Greedy approach to influence maximization -- 3.2.1 Greedy algorithm for influence maximization -- 3.2.2 Empirical evaluation of (G,k) -- 3.3 Scalable influence maximization -- 3.3.1 Reducing the number of influence spread evaluations -- 3.3.2 Speeding up influence computation -- 3.3.3 Other scalable influence maximization schemes -- | |
| 505 | 8 | _a4. Extensions to diffusion modeling and influence maximization -- 4.1 A data-based approach to influence maximization -- 4.2 Competitive influence modeling and maximization -- 4.2.1 Model extensions for competitive influence diffusion -- 4.2.2 Maximization problems for competitive influence diffusion -- 4.2.3 Endogenous competition -- 4.2.4 A new frontier, the host perspective -- 4.3 Influence, adoption, and profit -- 4.3.1 Influence vs. adoption -- 4.3.2 Influence vs. profit -- 4.4 Other extensions -- | |
| 505 | 8 | _a5. Learning propagation models -- 5.1 Basic models -- 5.2 IC model -- 5.3 Threshold models -- 5.3.1 Static models -- 5.3.2 Does influence remain static? -- 5.3.3 Continuous time models -- 5.3.4 Discrete time models -- 5.3.5 Are all objects equally influence prone? -- 5.3.6 Algorithms -- 5.3.7 Experimental validation -- 5.3.8 Discussion -- | |
| 505 | 8 | _a6. Data and software for information/influence: propagation research -- 6.1 Types of datasets -- 6.2 Propagation of information "memes" -- 6.2.1 Microblogging -- 6.2.2 Newspapers/blogs/etc. -- 6.3 Propagation of other actions -- 6.3.1 Consumption/appraisal platforms -- 6.3.2 User-generated content sharing/voting -- 6.3.3 Community membership as action -- 6.3.4 Cross-provider data -- 6.3.5 Phone logs -- 6.4 Network-only datasets -- 6.4.1 Citation networks -- 6.4.2 Other networks -- 6.5 Other off-line datasets -- 6.6 Publishing your own datasets -- 6.7 Software tools -- 6.7.1 Graph software tools -- 6.7.2 Propagation software tools -- 6.7.3 Visualization -- 6.8 Conclusions -- | |
| 505 | 8 | _a7. Conclusion and challenges -- 7.1 Application-specific challenges -- 7.1.1 Prove value for advertising/marketing -- 7.1.2 Learn to design for virality -- 7.1.3 Correct for sampling biases -- 7.1.4 Contribute to other applications -- 7.2 Technical challenges -- 7.3 Conclusions -- | |
| 505 | 8 | _aA. Notational conventions -- Bibliography -- Authors' biographies -- Index. | |
| 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 | _aResearch on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on November 13, 2013). | ||
| 650 | 0 | _aOnline social networks. | |
| 650 | 0 |
_aSocial influence _xMathematical models. |
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| 653 | _asocial networks | ||
| 653 | _asocial influence | ||
| 653 | _ainformation and influence diffusion | ||
| 653 | _astochastic diffusion models | ||
| 653 | _ainfluence maximization | ||
| 653 | _alearning of propagation models | ||
| 653 | _aviral marketing | ||
| 653 | _acompetitive influence diffusion | ||
| 653 | _agame theory | ||
| 653 | _acomputational complexity | ||
| 653 | _aapproximation algorithms | ||
| 653 | _aheuristic algorithms | ||
| 653 | _ascalability | ||
| 700 | 1 |
_aLakshmanan, Laks V. S., _d1959-, _eauthor. |
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| 700 | 1 |
_aCastillo, Carlos, _d1977-, _eauthor. |
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| 776 | 0 | 8 |
_iPrint version: _z9781627051156 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on data management ; _v# 37. _x2153-5426 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812846 |
| 856 | 4 | 0 |
_3Abstract with links to full text _uhttp://dx.doi.org/10.2200/S00527ED1V01Y201308DTM037 |
| 999 |
_c562033 _d562033 |
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