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Graph theoretical analysis of complex networks in the brain

Overview of attention for article published in Nonlinear Biomedical Physics, July 2007
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

1 blog
4 tweeters
1 Facebook page
1 Google+ user
2 Redditors


601 Dimensions

Readers on

914 Mendeley
9 CiteULike
4 Connotea
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Graph theoretical analysis of complex networks in the brain
Published in
Nonlinear Biomedical Physics, July 2007
DOI 10.1186/1753-4631-1-3
Pubmed ID

Cornelis J Stam, Jaap C Reijneveld


Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 914 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 26 3%
Netherlands 13 1%
Germany 13 1%
United Kingdom 12 1%
Brazil 10 1%
Spain 6 <1%
India 5 <1%
Canada 4 <1%
Portugal 2 <1%
Other 32 4%
Unknown 791 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 299 33%
Researcher 166 18%
Student > Master 145 16%
Student > Bachelor 52 6%
Professor > Associate Professor 49 5%
Other 159 17%
Unknown 44 5%
Readers by discipline Count As %
Engineering 142 16%
Neuroscience 125 14%
Agricultural and Biological Sciences 121 13%
Psychology 106 12%
Computer Science 99 11%
Other 218 24%
Unknown 103 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 18 November 2018.
All research outputs
of 13,865,625 outputs
Outputs from Nonlinear Biomedical Physics
of 18 outputs
Outputs of similar age
of 120,349 outputs
Outputs of similar age from Nonlinear Biomedical Physics
of 1 outputs
Altmetric has tracked 13,865,625 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 18 research outputs from this source. They receive a mean Attention Score of 3.6. This one scored the same or higher as 17 of them.
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 120,349 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 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them