DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ok, Jeongseul | ko |
dc.contributor.author | Jin, Young Mi | ko |
dc.contributor.author | Shin, Jinwoo | ko |
dc.contributor.author | Yi, Yung | ko |
dc.date.accessioned | 2017-03-28T05:36:24Z | - |
dc.date.available | 2017-03-28T05:36:24Z | - |
dc.date.created | 2016-11-21 | - |
dc.date.created | 2016-11-21 | - |
dc.date.created | 2016-11-21 | - |
dc.date.issued | 2016-12 | - |
dc.identifier.citation | IEEE-ACM TRANSACTIONS ON NETWORKING, v.24, no.6, pp.3798 - 3811 | - |
dc.identifier.issn | 1063-6692 | - |
dc.identifier.uri | http://hdl.handle.net/10203/220749 | - |
dc.description.abstract | A variety of models have been proposed and analyzed to understand how a new innovation (e.g., a technology, a product, or even a behavior) diffuses over a social network, broadly classified into either of epidemic-based or game-based ones. In this paper, we consider a game-based model, where each individual makes a selfish, rational choice in terms of its payoff in adopting the new innovation, but with some noise. We address the following two questions on the diffusion speed of a new innovation under the game-based model: 1) what is a good subset of individuals to seed for reducing the diffusion time significantly, i.e., convincing them to preadopt a new innovation and 2) how much diffusion time can be reduced by such a good seeding. For 1), we design near-optimal polynomial-time seeding algorithms for three representative classes of social network models, Erdos-Renyi,planted partition and geometrically structured graphs, and provide their performance guarantees in terms of approximation and complexity. For 2), we asymptotically quantify the diffusion time for these graph topologies; further derive the seed budget threshold above which the diffusion time is dramatically reduced, i.e., phase transition of diffusion time. Furthermore, based on our theoretical findings, we propose a practical seeding algorithm, called Practical Partitioning and Seeding (PrPaS) and demonstrate that PrPaS outperforms other baseline algorithms in terms of the diffusion speed over a real social network topology. We believe that our results provide new insights on how to seed over a social network depending on its connectivity structure, where individuals rationally adopt a new innovation. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | INFORMATION DIFFUSION | - |
dc.subject | BEHAVIOR | - |
dc.subject | GAMES | - |
dc.subject | EQUILIBRIA | - |
dc.subject | EPIDEMICS | - |
dc.subject | DYNAMICS | - |
dc.subject | SPREAD | - |
dc.title | On Maximizing Diffusion Speed Over Social Networks With Strategic Users | - |
dc.type | Article | - |
dc.identifier.wosid | 000391727900042 | - |
dc.identifier.scopusid | 2-s2.0-84971463486 | - |
dc.type.rims | ART | - |
dc.citation.volume | 24 | - |
dc.citation.issue | 6 | - |
dc.citation.beginningpage | 3798 | - |
dc.citation.endingpage | 3811 | - |
dc.citation.publicationname | IEEE-ACM TRANSACTIONS ON NETWORKING | - |
dc.identifier.doi | 10.1109/TNET.2016.2556719 | - |
dc.contributor.localauthor | Shin, Jinwoo | - |
dc.contributor.localauthor | Yi, Yung | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Influence maximization | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | random seeding | - |
dc.subject.keywordPlus | INFORMATION DIFFUSION | - |
dc.subject.keywordPlus | BEHAVIOR | - |
dc.subject.keywordPlus | GAMES | - |
dc.subject.keywordPlus | EQUILIBRIA | - |
dc.subject.keywordPlus | EPIDEMICS | - |
dc.subject.keywordPlus | DYNAMICS | - |
dc.subject.keywordPlus | SPREAD | - |
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