Ebook: Temporal Networks

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.

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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.

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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.
Content:
Front Matter....Pages i-viii
Temporal Networks as a Modeling Framework....Pages 1-14
Graph Metrics for Temporal Networks....Pages 15-40
Burstiness: Measures, Models, and Dynamic Consequences....Pages 41-64
Temporal Scale of Dynamic Networks....Pages 65-94
Models, Entropy and Information of Temporal Social Networks....Pages 95-117
Temporal Motifs....Pages 119-133
Applications of Temporal Graph Metrics to Real-World Networks....Pages 135-159
Spreading Dynamics Following Bursty Activity Patterns....Pages 161-174
Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics....Pages 175-190
Temporal Networks of Face-to-Face Human Interactions....Pages 191-216
Social Insects: A Model System for Network Dynamics....Pages 217-244
Self-Exciting Point Process Modeling of Conversation Event Sequences....Pages 245-264
Infering and Calibrating Triadic Closure in a Dynamic Network....Pages 265-282
Dynamic Communicability Predicts Infectiousness....Pages 283-294
Random Walks on Stochastic Temporal Networks....Pages 295-313
A Temporal Network Version of Watts’s Cascade Model....Pages 315-329
Timing Interactions in Social Simulations: The Voter Model....Pages 331-352

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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.
Content:
Front Matter....Pages i-viii
Temporal Networks as a Modeling Framework....Pages 1-14
Graph Metrics for Temporal Networks....Pages 15-40
Burstiness: Measures, Models, and Dynamic Consequences....Pages 41-64
Temporal Scale of Dynamic Networks....Pages 65-94
Models, Entropy and Information of Temporal Social Networks....Pages 95-117
Temporal Motifs....Pages 119-133
Applications of Temporal Graph Metrics to Real-World Networks....Pages 135-159
Spreading Dynamics Following Bursty Activity Patterns....Pages 161-174
Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics....Pages 175-190
Temporal Networks of Face-to-Face Human Interactions....Pages 191-216
Social Insects: A Model System for Network Dynamics....Pages 217-244
Self-Exciting Point Process Modeling of Conversation Event Sequences....Pages 245-264
Infering and Calibrating Triadic Closure in a Dynamic Network....Pages 265-282
Dynamic Communicability Predicts Infectiousness....Pages 283-294
Random Walks on Stochastic Temporal Networks....Pages 295-313
A Temporal Network Version of Watts’s Cascade Model....Pages 315-329
Timing Interactions in Social Simulations: The Voter Model....Pages 331-352
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