© 2013

Temporal Networks

  • Petter Holme
  • Jari Saramäki

Part of the Understanding Complex Systems book series (UCS)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Petter Holme, Jari Saramäki
    Pages 1-14
  3. Vincenzo Nicosia, John Tang, Cecilia Mascolo, Mirco Musolesi, Giovanni Russo, Vito Latora
    Pages 15-40
  4. Byungjoon Min, K.-I. Goh
    Pages 41-64
  5. Rajmonda Sulo Caceres, Tanya Berger-Wolf
    Pages 65-94
  6. Kun Zhao, Márton Karsai, Ginestra Bianconi
    Pages 95-117
  7. Lauri Kovanen, Márton Karsai, Kimmo Kaski, János Kertész, Jari Saramäki
    Pages 119-133
  8. John Tang, Ilias Leontiadis, Salvatore Scellato, Vincenzo Nicosia, Cecilia Mascolo, Mirco Musolesi et al.
    Pages 135-159
  9. Alain Barrat, Ciro Cattuto
    Pages 191-216
  10. Daniel Charbonneau, Benjamin Blonder, Anna Dornhaus
    Pages 217-244
  11. Naoki Masuda, Taro Takaguchi, Nobuo Sato, Kazuo Yano
    Pages 245-264
  12. Alexander V. Mantzaris, Desmond J. Higham
    Pages 265-282
  13. Alexander V. Mantzaris, Desmond J. Higham
    Pages 283-294
  14. Till Hoffmann, Mason A. Porter, Renaud Lambiotte
    Pages 295-313
  15. Fariba Karimi, Petter Holme
    Pages 315-329
  16. Juan Fernández-Gracia, Víctor M. Eguíluz, Maxi San Miguel
    Pages 331-352

About this book


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.


Bursty Acitvity Patterns Complex Temporal Networks Spreading Phenomena in Social Networks Temporal Graph Metrics Time-Varying Graphs and Networks

Editors and affiliations

  • Petter Holme
    • 1
  • Jari Saramäki
    • 2
  1. 1.Ice Lab, Department of PhysicsUmeå UniversityUmeåSweden
  2. 2.and Computational Sciences (BECS), Department of Biomedical EngineeringAalto UniversityAaltoFinland

Bibliographic information