Social Networks with Rich Edge Semantics
Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negativ...
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CRC Press
2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1578904&site=ehost-live header: @attributes: shortDbName: nlebk uiTerm: 1578904 longDbName: eBook Collection (EBSCOhost) uiTag: AN controlInfo: bkinfo: btl: Social Networks with Rich Edge Semantics aug: au: Quan Zheng David Skillicorn sertl: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series isbn: 9781138032439 9780367573256 9781315390604 9781315390611 9781315390628 imageinfo: pubinfo: dt: @attributes: year: 2017 month: 01 day: 01 dtAvail: @attributes: year: 2017 month: 09 day: 08 ed: First edition pub: CRC Press pubContract: CRC Press (Unlimited) place: Boca Raton, FL price: 0.01 limitsGroup: maxCheckoutDays: 1500 copyPages: -1 pda: N printPagesOffline: 60 printPagesOnline: 60 previewPages: 10000 prePubGroup: dewey: @attributes: class: 302.3015118 item: 302 .3015118 lc: @attributes: class: TK5105.88815 .Z54 2017 item: TK 5105 .88815 .Z54 2017 artinfo: ui: 1578904 993984779 formats: fmt: @attributes: type: EB doid: NL$1578904$PDF caption: PDF download: Y tig: atl: Social Networks with Rich Edge Semantics ptl: Social Networks with Rich Edge Semantics aug: au: Quan Zheng David Skillicorn su: Semantic Web Social networks--Mathematical models Social media sug: subj: BUSINESS & ECONOMICS / Statistics COMPUTERS / General COMPUTERS / Programming / Games COMPUTERS / Data Science / Data Analytics COMPUTERS / Machine Theory Semantic Web Social networks--Mathematical models Social media ab: Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets.Features Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks. pubtype: eBook doctype: Book ougenre: Book language: English copyright: @attributes: flag: N copyrightText: holdings: @attributes: islocal: N |
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