LEADER 04092oam 2200577I 450 001 9910316450403321 005 20230126215711.0 010 $a9781315390604 010 $a1315390604 010 $a9781315390628 010 $a1315390620 010 $a9781315390611 010 $a1315390612 024 7 $a10.1201/9781315390628 035 $a(CKB)4100000000267830 035 $a(MiAaPQ)EBC4947403 035 $a(OCoLC)993984779 035 $a(ScCtBLL)946af449-1a4c-467d-a400-0f35921ee596 035 $a(Perlego)2330294 035 $a(oapen)doab32179 035 $a(EXLCZ)994100000000267830 100 $a20180706h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aSocial Networks with Rich Edge Semantics /$fQuan Zheng, David Skillicorn 205 $aFirst edition. 210 $cTaylor & Francis$d2017 210 1$aBoca Raton, FL :$cCRC Press,$d[2017] 210 4$dİ2017 215 $a1 online resource (210 pages) $cillustrations, tables 225 1 $aChapman & Hall/CRC Data Mining and Knowledge Discovery Series 311 08$a9781138032439 311 08$a1138032433 320 $aIncludes bibliographical references and index. 327 $achapter 1 introduction -- chapter 2 the core model -- chapter 3 background -- chapter 4 modelling relationships of different types -- chapter 5 modelling asymmetric relationships -- chapter 6 modelling asymmetric relationships with multiple types -- chapter 7 modelling relationships that change over time -- chapter 8 modelling positive and negative relationships -- chapter 9 signed graph-based semi-supervised learning -- chapter 10 combining directed and signed embeddings -- chapter 11 summary. 330 $a"Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets.FeaturesIntroduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over timePresents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzedIncludes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriateShows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a nodeIllustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groupsSuitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks."--Provided by publisher. 410 0$aChapman & Hall/CRC data mining and knowledge discovery series. 606 $aSocial networks$xMathematical models 615 0$aSocial networks$xMathematical models. 676 $a302.3 700 $aZheng$b Quan$0340556 702 $aSkillicorn$b David B. 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910316450403321 996 $aSocial Networks with Rich Edge Semantics$91932796 997 $aUNINA