Temporal networks
Temporal networks
editores Petter Holme y Jari Saramäki
- Heidelberg (Baden-Wurtemberg, Alemania) Springer 2013
- viii, 352 páginas ilustraciones y gráficas
- Springer complexity Understanding Complex Systems .
Incluye referencias bibliográficas
Temporal Networks as a Modeling Framework Graph Metrics for Temporal Networks Burstiness: Measures, Models, and Dynamic Consequences Temporal Scale of Dynamic Networks Models, Entropy and Information of Temporal Social Networks Temporal Motifs Applications of Temporal Graph Metrics to Real-World Networks Spreading Dynamics Following Bursty Activity Patterns Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics Temporal Networks of Face-to-Face Human Interactions Social Insects: A Model System for Network Dynamics Self-Exciting Point Process Modeling of Conversation Event Sequences Infering and Calibrating Triadic Closure in a Dynamic Network Dynamic Communicability Predicts Infectiousness Random Walks on Stochastic Temporal Networks A Temporal Network Version of Watts's Cascade Model Timing Interactions in Social Simulations: The Voter Model Petter Holme, Jari Saramäki Vincenzo Nicosia, John Tang, Cecilia Mascolo, Mirco Musolesi Byungjoon Min, K.-I. Goh Rajmonda Sulo Caceres, Tanya Berger-Wolf Kun Zhao, Márton Karsai, Ginestra Bianconi Lauri Kovanen, Márton Karsai, Kimmo Kaski, János Kertész John Tang, Ilias Leontiadis, Salvatore Scellato, Vincenzo Nicosia Alexei Vazquez Giovanna Miritello, Rubén Lara, Esteban Moro Alain Barrat, Ciro Cattuto Daniel Charbonneau, Benjamin Blonder, Anna Dornhaus Naoki Masuda, Taro Takaguchi, Nobuo Sato, Kazuo Yano Alexander V. Mantzaris, Desmond J. Higham Alexander V. Mantzaris, Desmond J. Higham Till Hoffmann, Mason A. Porter, Renaud Lambiotte Fariba Karimi, Petter Holme Juan Fernández-Gracia, Víctor M. Eguíluz, Maxi San Miguel
The 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.
9783642364600
Análisis de sistemas
Complejidad computacional
Incluye referencias bibliográficas
Temporal Networks as a Modeling Framework Graph Metrics for Temporal Networks Burstiness: Measures, Models, and Dynamic Consequences Temporal Scale of Dynamic Networks Models, Entropy and Information of Temporal Social Networks Temporal Motifs Applications of Temporal Graph Metrics to Real-World Networks Spreading Dynamics Following Bursty Activity Patterns Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics Temporal Networks of Face-to-Face Human Interactions Social Insects: A Model System for Network Dynamics Self-Exciting Point Process Modeling of Conversation Event Sequences Infering and Calibrating Triadic Closure in a Dynamic Network Dynamic Communicability Predicts Infectiousness Random Walks on Stochastic Temporal Networks A Temporal Network Version of Watts's Cascade Model Timing Interactions in Social Simulations: The Voter Model Petter Holme, Jari Saramäki Vincenzo Nicosia, John Tang, Cecilia Mascolo, Mirco Musolesi Byungjoon Min, K.-I. Goh Rajmonda Sulo Caceres, Tanya Berger-Wolf Kun Zhao, Márton Karsai, Ginestra Bianconi Lauri Kovanen, Márton Karsai, Kimmo Kaski, János Kertész John Tang, Ilias Leontiadis, Salvatore Scellato, Vincenzo Nicosia Alexei Vazquez Giovanna Miritello, Rubén Lara, Esteban Moro Alain Barrat, Ciro Cattuto Daniel Charbonneau, Benjamin Blonder, Anna Dornhaus Naoki Masuda, Taro Takaguchi, Nobuo Sato, Kazuo Yano Alexander V. Mantzaris, Desmond J. Higham Alexander V. Mantzaris, Desmond J. Higham Till Hoffmann, Mason A. Porter, Renaud Lambiotte Fariba Karimi, Petter Holme Juan Fernández-Gracia, Víctor M. Eguíluz, Maxi San Miguel
The 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.
9783642364600
Análisis de sistemas
Complejidad computacional