2018
DOI: 10.1038/s41598-018-30472-z
|View full text |Cite
|
Sign up to set email alerts
|

Topological structures are consistently overestimated in functional complex networks

Abstract: Functional complex networks have meant a pivotal change in the way we understand complex systems, being the most outstanding one the human brain. These networks have classically been reconstructed using a frequentist approach that, while simple, completely disregards the uncertainty that derives from data finiteness. We provide here an alternative solution based on Bayesian inference, with link weights treated as random variables described by probability distributions, from which ensembles of networks are samp… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

3
11
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
6
1

Relationship

3
4

Authors

Journals

citations
Cited by 9 publications
(14 citation statements)
references
References 37 publications
3
11
0
Order By: Relevance
“…Clustering coefficient and mean shortest path are oftentimes used to decide upon a network's small-worldness (Bassett and Bullmore, 2006 ), and this property has repeatedly been reported for networks from diverse scientific disciplines. Given the many factors that impact on clustering coefficient and mean shortest path, however, these findings continue to be matter of considerable debate (Bialonski et al, 2010 ; Gastner and Ódor, 2016 ; Hilgetag and Goulas, 2016 ; Papo et al, 2016 ; Hlinka et al, 2017 ; Zanin et al, 2018 ).…”
Section: Challenges With Deriving and Characterizing An Integratedmentioning
confidence: 99%
“…Clustering coefficient and mean shortest path are oftentimes used to decide upon a network's small-worldness (Bassett and Bullmore, 2006 ), and this property has repeatedly been reported for networks from diverse scientific disciplines. Given the many factors that impact on clustering coefficient and mean shortest path, however, these findings continue to be matter of considerable debate (Bialonski et al, 2010 ; Gastner and Ódor, 2016 ; Hilgetag and Goulas, 2016 ; Papo et al, 2016 ; Hlinka et al, 2017 ; Zanin et al, 2018 ).…”
Section: Challenges With Deriving and Characterizing An Integratedmentioning
confidence: 99%
“…Finally, this has been shown to have important consequences in the observed topological features [27,57].…”
Section: Introductionmentioning
confidence: 92%
“…Two scenarios are compared, i.e. a standard frequentist approach, in which connectivity is represented as a single number, and a Bayesian one, yielding a complete probability distribution [57]. We firstly recover some previously reported results, i.e.…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, Bayesian inference-unlike frequentist-estimates a full probability model, including hypothesis testing. Disregarding such uncertainty may lead to the detection of wrong topological structures, and specifically to an overestimation of the presence of regularities and non-trivial (i.e., non-random) structures (Zanin, Belkoura, Gomez, Alfaro, & Cano, 2018).…”
Section: Classical Versus Bayesian Reconstructionmentioning
confidence: 99%