β Scribed by Giulio Cimini, Rossana Mastrandrea, Tiziano Squartini
No coin nor oath required. For personal study only.
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.
Cover
Title Page
Copyright Page
Reconstructing Networks
Contents
1 Introduction
2 Network Reconstruction at the Macroscale
Macroscale Properties: An Overview
Binary Properties
Weighted Properties
Macroscale Reconstruction of Economic and Financial Networks: A Quick Historical Survey
The Exponential Random Graphs Framework
The Best-Performing Reconstruction Method
Testing Reconstruction at the Macroscale
Quantifying Systemic Risk
3 Network Reconstruction at the Mesoscale
Motifs: The Building Blocks of Networks
Community Structure
The Stochastic Block Model
The Core-Periphery Organization
The Bow-Tie Organization
4 Network Reconstruction at the Microscale
The Link Prediction Framework
Similarity-Based Methods
Model-Based Methods
Hyperbolic Latent Space Models
Network Reconstruction from Noisy Data
Quality Metrics for Link Prediction
5 Conclusions
Appendix A Reconstructing Bipartite Networks
Appendix B Model Selection: A Quick Look at AIC and BIC
References
Disclaimer
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