Title |
Knotty-Centrality: Finding the Connective Core of a Complex Network
|
---|---|
Published in |
PLOS ONE, May 2012
|
DOI | 10.1371/journal.pone.0036579 |
Pubmed ID | |
Authors |
Murray Shanahan, Mark Wildie |
Abstract |
A network measure called knotty-centrality is defined that quantifies the extent to which a given subset of a graph's nodes constitutes a densely intra-connected topologically central connective core. Using this measure, the knotty centre of a network is defined as a sub-graph with maximal knotty-centrality. A heuristic algorithm for finding subsets of a network with high knotty-centrality is presented, and this is applied to previously published brain structural connectivity data for the cat and the human, as well as to a number of other networks. The cognitive implications of possessing a connective core with high knotty-centrality are briefly discussed. |
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
Egypt | 1 | 50% |
Australia | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 5% |
United States | 3 | 4% |
Germany | 1 | 1% |
India | 1 | 1% |
France | 1 | 1% |
Japan | 1 | 1% |
Finland | 1 | 1% |
Unknown | 62 | 84% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 23 | 31% |
Researcher | 13 | 18% |
Professor > Associate Professor | 10 | 14% |
Student > Bachelor | 7 | 9% |
Professor | 5 | 7% |
Other | 11 | 15% |
Unknown | 5 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 11 | 15% |
Neuroscience | 8 | 11% |
Agricultural and Biological Sciences | 7 | 9% |
Psychology | 7 | 9% |
Medicine and Dentistry | 5 | 7% |
Other | 24 | 32% |
Unknown | 12 | 16% |