Temporal Network Theory

  • Petter Holme
  • Jari Saramäki

Part of the Computational Social Sciences book series (CSS)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Petter Holme, Jari Saramäki
    Pages 1-24
  3. Matthieu Latapy, Clémence Magnien, Tiphaine Viard
    Pages 49-64
  4. Claudio D. G. Linhares, Jean R. Ponciano, Jose Gustavo S. Paiva, Bruno A. N. Travençolo, Luis E. C. Rocha
    Pages 83-105
  5. Jari Saramäki, Mikko Kivelä, Márton Karsai
    Pages 107-128
  6. Davide Vega, Matteo Magnani
    Pages 147-160
  7. Hang-Hyun Jo, Takayuki Hiraoka
    Pages 161-179
  8. Remy Cazabet, Giulio Rossetti
    Pages 181-197
  9. Huijuan Wang, Xiu-Xiu Zhan
    Pages 199-217
  10. Andreas Koher, James P. Gleeson, Philipp Hövel
    Pages 235-252
  11. Tomokatsu Onaga, James P. Gleeson, Naoki Masuda
    Pages 253-267
  12. Russell Jeter, Maurizio Porfiri, Igor Belykh
    Pages 269-304
  13. Kaiyuan Sun, Enrico Ubaldi, Jie Zhang, Márton Karsai, Nicola Perra
    Pages 305-324
  14. Dane Taylor, Mason A. Porter, Peter J. Mucha
    Pages 325-344
  15. Back Matter
    Pages 369-375

About this book


This book focuses on the theoretical side of temporal network research and gives an overview of the state of the art in the field. Curated by two pioneers in the field who have helped to shape it, the book contains contributions from many leading researchers. Temporal networks fill the border area between network science and time-series analysis and are relevant for the modeling of epidemics, optimization of transportation and logistics, as well as understanding biological phenomena.

Network theory has proven, over the past 20 years to be one of the most powerful tools for the study and analysis of complex systems. Temporal network theory is perhaps the most recent significant development in the field in recent years, with direct applications to many of the "big data" sets. This monograph will appeal to students, researchers and professionals alike interested in theory and temporal networks, a field that has grown tremendously over the last decade.


time series analysis computational social science computational neuroscience big data and network theory time varying networks epidemic spreading processes network theory and modeling frameworks and tools measures of temporal network structure network theory and mesoscopic structures network theory and dynamic processes network epidemic spreading

Editors and affiliations

  1. 1.Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative ResearchTokyo Institute of TechnologyTokyoJapan
  2. 2.Department of Computer ScienceAalto UniversityEspooFinland

Bibliographic information