LEADER 03638nam 2200577Ia 450 001 9910437980603321 005 20200520144314.0 010 $a3-319-00110-8 024 7 $a10.1007/978-3-319-00110-4 035 $a(CKB)2670000000360670 035 $a(EBL)1205642 035 $a(SSID)ssj0000908948 035 $a(PQKBManifestationID)11582442 035 $a(PQKBTitleCode)TC0000908948 035 $a(PQKBWorkID)10913300 035 $a(PQKB)11240999 035 $a(DE-He213)978-3-319-00110-4 035 $a(MiAaPQ)EBC1205642 035 $a(PPN)169137406 035 $a(EXLCZ)992670000000360670 100 $a20130305d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aTemporal patterns of communication in social networks /$fGiovanna Miritello 205 $a1st ed. 2013. 210 $aNew York $cSpringer$d2013 215 $a1 online resource (153 p.) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 300 $aDescription based upon print version of record. 311 $a3-319-03341-7 311 $a3-319-00109-4 320 $aIncludes bibliographical references. 327 $aIntroduction and Motivation -- Social and Communication Networks -- Social Strategies in Communication Networks -- Predicting Tie Creation and Decay -- Information Spreading on Communication Networks -- Conclusion, contributions and vision for the future -- Data and Materials. 330 $aThe main interest of this research has been in understanding and characterizing large networks of human interactions as continuously changing objects. In fact, although many real social networks are dynamic networks whose elements and properties continuously change over time, traditional approaches to social network analysis are essentially static, thus neglecting all temporal aspects. Specifically, we have investigated the role that temporal patterns of human interaction play in three main fields of social network analysis and data mining: characterization of time (or attention) allocation in social networks, prediction of link decay/persistence, and information spreading. In order to address this we analyzed large anonymized data sets of phone call communication traces over long periods of time. Access to these observations was granted by Telefonica Research, Spain. The findings that emerge from our research indicate that the observed heterogeneities and correlations of human temporal patterns of interaction significantly affect the traditional view of social networks, shifting from a very steady to a highly complex entity. Since structure and dynamics are tightly coupled, they cannot be disentangled in the analysis and modeling of human behavior, though traditional models seek to do so. Our results impact not only the way in which social network are traditionally characterized, but more importantly also the understanding and modeling phenomena such as group formation, spread of epidemics, and the dissemination of ideas, opinions and information. 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 606 $aCommunications software$xResearch 606 $aSocial networks$xResearch 615 0$aCommunications software$xResearch. 615 0$aSocial networks$xResearch. 676 $a621 700 $aMiritello$b Giovanna$0916025 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437980603321 996 $aTemporal Patterns of Communication in Social Networks$92053515 997 $aUNINA