000			03813cam a2200289Ka 4500
005			20150604155043.0
008			130605s2013 gw ad frn 000 0 eng d
020			_a9783642364600
035			_a(OCoLC)846838827
040			_aCO-BoUCM _cJose A. Gerena (modificó) _d[Nuevo] _d[Nuevo]
041	0		_aeng
245	0	0	_aTemporal networks _ceditores Petter Holme y Jari Saramäki
260			_aHeidelberg (Baden-Wurtemberg, Alemania) _bSpringer _c2013
300			_aviii, 352 páginas _bilustraciones y gráficas
490	0		_aSpringer complexity
490	0		_aUnderstanding Complex Systems
504			_aIncluye referencias bibliográficas
505	0		_tTemporal Networks as a Modeling Framework _rPetter Holme, Jari Saramäki _tGraph Metrics for Temporal Networks _rVincenzo Nicosia, John Tang, Cecilia Mascolo, Mirco Musolesi _tBurstiness: Measures, Models, and Dynamic Consequences _rByungjoon Min, K.-I. Goh _tTemporal Scale of Dynamic Networks _rRajmonda Sulo Caceres, Tanya Berger-Wolf _tModels, Entropy and Information of Temporal Social Networks _rKun Zhao, Márton Karsai, Ginestra Bianconi _tTemporal Motifs _rLauri Kovanen, Márton Karsai, Kimmo Kaski, János Kertész _tApplications of Temporal Graph Metrics to Real-World Networks _rJohn Tang, Ilias Leontiadis, Salvatore Scellato, Vincenzo Nicosia _tSpreading Dynamics Following Bursty Activity Patterns _rAlexei Vazquez _tTime Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics _rGiovanna Miritello, Rubén Lara, Esteban Moro _tTemporal Networks of Face-to-Face Human Interactions _rAlain Barrat, Ciro Cattuto _tSocial Insects: A Model System for Network Dynamics _rDaniel Charbonneau, Benjamin Blonder, Anna Dornhaus _tSelf-Exciting Point Process Modeling of Conversation Event Sequences _rNaoki Masuda, Taro Takaguchi, Nobuo Sato, Kazuo Yano _tInfering and Calibrating Triadic Closure in a Dynamic Network _rAlexander V. Mantzaris, Desmond J. Higham _tDynamic Communicability Predicts Infectiousness _rAlexander V. Mantzaris, Desmond J. Higham _tRandom Walks on Stochastic Temporal Networks _rTill Hoffmann, Mason A. Porter, Renaud Lambiotte _tA Temporal Network Version of Watts's Cascade Model _rFariba Karimi, Petter Holme _tTiming Interactions in Social Simulations: The Voter Model _rJuan Fernández-Gracia, Víctor M. Eguíluz, Maxi San Miguel
520			_aThe concept of temporal networks is an extension of complex networks as a modeling framework to include information on when interactions between nodes happen. Many studies of the last decade examine how the static network structure affect dynamic systems on the network. In this traditional approach the temporal aspects are pre-encoded in the dynamic system model. Temporal-network methods, on the other hand, lift the temporal information from the level of system dynamics to the mathematical representation of the contact network itself. This framework becomes particularly useful for cases where there is a lot of structure and heterogeneity both in the timings of interaction events and the network topology. The advantage compared to common static network approaches is the ability to design more accurate models in order to explain and predict large-scale dynamic phenomena (such as, e.g., epidemic outbreaks and other spreading phenomena). On the other hand, temporal network methods are mathematically and conceptually more challenging. This book is intended as a first introduction and state-of-the art overview of this rapidly emerging field.
650		7	_aAnálisis de sistemas _98971
650		7	_aComplejidad computacional _911274
690			_942853 _aInformática
700	1		_aHolme, Petter _eeditor _955260
700	1		_aSaramäki, Jari _eeditor _955261
942			_a1 _cLIBRO _e1 _mT288 _2DEWEY _h003
999			_c184406 _d184406