Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.
Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.
Reihe
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
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Worked examples or Exercises
Dateigröße
ISBN-13
978-1-108-80876-7 (9781108808767)
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Schweitzer Klassifikation
1. Introduction; 2. Network reconstruction at the macroscale; 3. Network reconstruction at the mesoscale; 4. Network reconstruction at the microscale; 5. Conclusions; Appendix A Reconstructing bipartite networks; Appendix B Model selection: a quick look at AIC and BIC; References.
