LEADER 04020nam 22007575 450 001 9910299202103321 005 20230616020223.0 010 $a3-319-23105-1 024 7 $a10.1007/978-3-319-23105-1 035 $a(CKB)3710000000476932 035 $a(EBL)4178526 035 $a(SSID)ssj0001584240 035 $a(PQKBManifestationID)16265653 035 $a(PQKBTitleCode)TC0001584240 035 $a(PQKBWorkID)14866395 035 $a(PQKB)10220509 035 $a(DE-He213)978-3-319-23105-1 035 $a(MiAaPQ)EBC4178526 035 $a(PPN)190522852 035 $a(EXLCZ)993710000000476932 100 $a20150916d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDiffusion in social networks /$fby Paulo Shakarian, Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo 205 $a1st ed. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d[2015] 210 4$d©2015 215 $a1 online resource (110 p.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 300 $aDescription based upon print version of record. 311 1 $a3-319-23104-9 311 1 $a9783319231044 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction -- The SIR Model and Identification of Spreaders -- The Tipping Model and the Minimum Seed Problem -- The Independent Cascade and Linear Threshold Models -- Logic Programming Based Diffusion Models -- Evolutionary Graph Theory -- Examining Diffusion in the Real World -- Conclusion. 330 $aThis book presents the leading models of social network diffusion that are used to demonstrate the spread of disease, ideas, and behavior. It introduces diffusion models from the fields of computer science (independent cascade and linear threshold), sociology (tipping models), physics (voter models), biology (evolutionary models), and epidemiology (SIR/SIS and related models). A variety of properties and problems related to these models are discussed including identifying seeds sets to initiate diffusion, game theoretic problems, predicting diffusion events, and more. The book explores numerous connections between social network diffusion research and artificial intelligence through topics such as agent-based modeling, logic programming, game theory, learning, and data mining. The book also surveys key empirical results in social network diffusion, and reviews the classic and cutting-edge research with a focus on open problems. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aIntel·ligčncia artificial$2lemac 606 $aXifratge (Informŕtica)$2lemac 606 $aXarxes socials en línia$2lemac 606 $aArtificial intelligence 606 $aData encryption (Computer science) 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aCryptology$3https://scigraph.springernature.com/ontologies/product-market-codes/I28020 615 7$aIntel·ligčncia artificial 615 7$aXifratge (Informŕtica) 615 7$aXarxes socials en línia 615 0$aArtificial intelligence. 615 0$aData encryption (Computer science). 615 14$aArtificial Intelligence. 615 24$aCryptology. 676 $a519.233 700 $aShakarian$b Paulo$4aut$4http://id.loc.gov/vocabulary/relators/aut$0791399 702 $aBhatnagar$b Abhivav$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aAleali$b Ashkan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aShaabani$b Elham$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGuo$b Ruocheng$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299202103321 996 $aDiffusion in Social Networks$92543142 997 $aUNINA