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001 978-3-319-00110-4
003 DE-He213
005 20140220082837.0
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008 130424s2013 gw | s |||| 0|eng d
020 _a9783319001104
_9978-3-319-00110-4
024 7 _a10.1007/978-3-319-00110-4
_2doi
050 4 _aQC1-QC999
072 7 _aPHU
_2bicssc
072 7 _aPBKD
_2bicssc
072 7 _aSCI064000
_2bisacsh
082 0 4 _a621
_223
100 1 _aMiritello, Giovanna.
_eauthor.
245 1 0 _aTemporal Patterns of Communication in Social Networks
_h[electronic resource] /
_cby Giovanna Miritello.
264 1 _aHeidelberg :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXIV, 153 p. 43 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
505 0 _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.
520 _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.
650 0 _aPhysics.
650 0 _aMathematics.
650 1 4 _aPhysics.
650 2 4 _aComplex Networks.
650 2 4 _aMathematics in the Humanities and Social Sciences.
650 2 4 _aCommunication Studies.
650 2 4 _aComplex Systems.
650 2 4 _aGame Theory, Economics, Social and Behav. Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319001098
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-00110-4
912 _aZDB-2-PHA
999 _c96356
_d96356