LEADER 04589nam 22007215 450 001 9910254840603321 005 20200630114221.0 010 $a3-319-53420-3 024 7 $a10.1007/978-3-319-53420-6 035 $a(CKB)3710000001186119 035 $a(DE-He213)978-3-319-53420-6 035 $a(MiAaPQ)EBC4851843 035 $a(PPN)200513427 035 $a(EXLCZ)993710000001186119 100 $a20170429d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTrends in Social Network Analysis $eInformation Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment /$fedited by Rokia Missaoui, Talel Abdessalem, Matthieu Latapy 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIII, 255 p. 90 illus., 68 illus. in color.) 225 1 $aLecture Notes in Social Networks,$x2190-5428 311 $a3-319-53419-X 320 $aIncludes bibliographical references at the end of each chapters. 327 $a1. The Perceived Assortativity of Social Networks: Methodological Problems and Solutions -- 2. A Parametric Study to Construct Time-aware Social Profiles -- 3. A Parametric Study to Construct Time-aware Social Profiles -- 4. The DEvOTION Algorithm for Delurking in Social Networks -- 5. Social Engineering Threat Assessment using a Multi-layered Graph-based Model -- 6. Through The Grapevine: A Comparison of News in Microblogs and Traditional Media -- 7. Prediction of Elevated Activity in Online Social Media Using Aggregated and Individualized Models -- 8. Unsupervised Link Prediction Based on Time Frames in Weighted-Directed Citation Networks -- 9. An Approach to Maximize the Influence Spread in Social Networks -- 10. Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm. 330 $aThe book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field. 410 0$aLecture Notes in Social Networks,$x2190-5428 606 $aData mining 606 $aSocial sciences?Data processing 606 $aSocial sciences?Computer programs 606 $aArtificial intelligence 606 $aDatabase management 606 $aPhysics 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aComputational Social Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/X34000 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aApplications of Graph Theory and Complex Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/P33010 615 0$aData mining. 615 0$aSocial sciences?Data processing. 615 0$aSocial sciences?Computer programs. 615 0$aArtificial intelligence. 615 0$aDatabase management. 615 0$aPhysics. 615 14$aData Mining and Knowledge Discovery. 615 24$aComputational Social Sciences. 615 24$aArtificial Intelligence. 615 24$aDatabase Management. 615 24$aApplications of Graph Theory and Complex Networks. 676 $a302.3 702 $aMissaoui$b Rokia$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAbdessalem$b Talel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLatapy$b Matthieu$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254840603321 996 $aTrends in Social Network Analysis$92540322 997 $aUNINA