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Evolution of Controllability in Interbank Networks

Overview of attention for article published in Scientific Reports, April 2013
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2 X users
facebook
1 Facebook page

Citations

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74 Dimensions

Readers on

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105 Mendeley
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2 CiteULike
Title
Evolution of Controllability in Interbank Networks
Published in
Scientific Reports, April 2013
DOI 10.1038/srep01626
Pubmed ID
Authors

Danilo Delpini, Stefano Battiston, Massimo Riccaboni, Giampaolo Gabbi, Fabio Pammolli, Guido Caldarelli

Abstract

The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected "hub" institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 2%
Italy 2 2%
Canada 1 <1%
Mexico 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 97 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 28%
Researcher 22 21%
Student > Master 15 14%
Professor 6 6%
Professor > Associate Professor 6 6%
Other 14 13%
Unknown 13 12%
Readers by discipline Count As %
Economics, Econometrics and Finance 18 17%
Physics and Astronomy 17 16%
Computer Science 14 13%
Business, Management and Accounting 10 10%
Engineering 9 9%
Other 22 21%
Unknown 15 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 May 2013.
All research outputs
#15,663,989
of 24,814,419 outputs
Outputs from Scientific Reports
#75,111
of 135,784 outputs
Outputs of similar age
#119,430
of 204,108 outputs
Outputs of similar age from Scientific Reports
#298
of 474 outputs
Altmetric has tracked 24,814,419 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 135,784 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 204,108 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 474 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.