Iterative procedure for network inference

Gloria Cecchini; Bjoern Schelter

doi:10.1016/j.cnsns.2020.105286

Iterative procedure for network inference

Gloria Cecchini^*, Bjoern Schelter

^*Corresponding author for this work

University of Florence

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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Abstract

When a network is reconstructed from data, two types of errors can occur: false positive and false negative errors about the presence or absence of links. In this paper, the vertex degree distribution of the true underlying network is analytically reconstructed using an iterative procedure. Such procedure is based on the inferred network and estimates for the probabilities α and β of type I and type II errors, respectively. The iteration procedure consists of choosing various values for α to perform the iteration steps of the network reconstruction. For the first step, the standard value for α of 0.05 can be chosen as an example. The result of this first step gives a first estimate of the network topology of interest. For the second iteration step the value for α is adjusted according to the findings of the first step. This procedure is iterated, ultimately leading to a reconstruction of the vertex degree distribution tailored to its previously unknown network topology.

Original language	English
Article number	105286
Number of pages	10
Journal	Communications in Nonlinear Science & Numerical Simulation
Volume	88
Early online date	28 Apr 2020
DOIs	https://doi.org/10.1016/j.cnsns.2020.105286
Publication status	Published - Sept 2020

Bibliographical note

Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642563.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Keywords

network inference
node degree distribution
false positive
false negative
statistical inference
False positive
Node degree distribution
Network inference
Statistical inference
MINIMUM DISTANCE
COMPLEX NETWORKS

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Cite this

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title = "Iterative procedure for network inference",

abstract = "When a network is reconstructed from data, two types of errors can occur: false positive and false negative errors about the presence or absence of links. In this paper, the vertex degree distribution of the true underlying network is analytically reconstructed using an iterative procedure. Such procedure is based on the inferred network and estimates for the probabilities α and β of type I and type II errors, respectively. The iteration procedure consists of choosing various values for α to perform the iteration steps of the network reconstruction. For the first step, the standard value for α of 0.05 can be chosen as an example. The result of this first step gives a first estimate of the network topology of interest. For the second iteration step the value for α is adjusted according to the findings of the first step. This procedure is iterated, ultimately leading to a reconstruction of the vertex degree distribution tailored to its previously unknown network topology.",

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author = "Gloria Cecchini and Bjoern Schelter",

note = "Acknowledgements This project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642563. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.",

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N2 - When a network is reconstructed from data, two types of errors can occur: false positive and false negative errors about the presence or absence of links. In this paper, the vertex degree distribution of the true underlying network is analytically reconstructed using an iterative procedure. Such procedure is based on the inferred network and estimates for the probabilities α and β of type I and type II errors, respectively. The iteration procedure consists of choosing various values for α to perform the iteration steps of the network reconstruction. For the first step, the standard value for α of 0.05 can be chosen as an example. The result of this first step gives a first estimate of the network topology of interest. For the second iteration step the value for α is adjusted according to the findings of the first step. This procedure is iterated, ultimately leading to a reconstruction of the vertex degree distribution tailored to its previously unknown network topology.

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KW - node degree distribution

KW - false positive

KW - false negative

KW - statistical inference

KW - False positive

KW - Node degree distribution

KW - Network inference

KW - Statistical inference

KW - MINIMUM DISTANCE

KW - COMPLEX NETWORKS

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Iterative procedure for network inference

Abstract

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Keywords

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