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The Polynomial Volume Law of Complex Networks in the Context of Local and Global Optimization

Overview of attention for article published in Scientific Reports, July 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
6 news outlets
blogs
2 blogs
twitter
16 X users
facebook
1 Facebook page
reddit
1 Redditor

Citations

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10 Dimensions

Readers on

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25 Mendeley
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Title
The Polynomial Volume Law of Complex Networks in the Context of Local and Global Optimization
Published in
Scientific Reports, July 2018
DOI 10.1038/s41598-018-29131-0
Pubmed ID
Authors

Franz-Benjamin Mocnik

Abstract

Many complex networks expose global hub structures: for some nodes, the number of incident edges far exceeds the average, leading to a small average shortest path length. Such 'small-world properties' are often guided by a scale-free power-law distribution of the node degrees, and self-organization inside the network has been identified as a reason driving the emergence of this structure. Small-world networks have recently raised lots of interest, because they capture the global topology of the World-Wide Web, metabolic, and social networks. While small-world networks reflect global structures, little attention is paid to the local structure of complex networks. In this article neighbourhoods are demonstrated to share a common local structure in many real complex networks, manifested by a polynomial volume law. This law can, in case of networks that are embedded in space, be explained in terms of the embedding and the properties of Euclidean space. A model of hierarchical spatial networks is introduced to examine the effect of global structures, in particular of hierarchies, on the polynomial volume law. It turns out that the law is robust against the coexistence of such global structures. The local structure of space and global optimization can both be found in transport, brain, and communication networks, which suggests the polynomial volume law, often in combination with hierarchies or other global optimization principles, to be a generic property inherent to many networks.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 20%
Student > Ph. D. Student 4 16%
Student > Bachelor 3 12%
Student > Master 2 8%
Professor 1 4%
Other 2 8%
Unknown 8 32%
Readers by discipline Count As %
Business, Management and Accounting 4 16%
Engineering 3 12%
Computer Science 3 12%
Earth and Planetary Sciences 2 8%
Social Sciences 2 8%
Other 6 24%
Unknown 5 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 65. 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 25 April 2019.
All research outputs
#614,415
of 24,287,697 outputs
Outputs from Scientific Reports
#6,738
of 132,007 outputs
Outputs of similar age
#13,750
of 334,129 outputs
Outputs of similar age from Scientific Reports
#173
of 3,606 outputs
Altmetric has tracked 24,287,697 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 132,007 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one has done particularly well, scoring higher than 94% of its peers.
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 334,129 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 3,606 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.