Title |
Deciphering Network Community Structure by Surprise
|
---|---|
Published in |
PLOS ONE, September 2011
|
DOI | 10.1371/journal.pone.0024195 |
Pubmed ID | |
Authors |
Rodrigo Aldecoa, Ignacio Marín |
Abstract |
The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 11% |
India | 2 | 11% |
Chile | 1 | 6% |
Bosnia and Herzegovina | 1 | 6% |
United States | 1 | 6% |
Spain | 1 | 6% |
Japan | 1 | 6% |
Unknown | 9 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 10 | 56% |
Scientists | 7 | 39% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 3% |
Japan | 4 | 3% |
India | 3 | 2% |
France | 2 | 1% |
Spain | 2 | 1% |
Turkey | 2 | 1% |
Italy | 2 | 1% |
Australia | 1 | <1% |
Malaysia | 1 | <1% |
Other | 5 | 3% |
Unknown | 123 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 42 | 28% |
Researcher | 27 | 18% |
Professor > Associate Professor | 18 | 12% |
Professor | 13 | 9% |
Student > Master | 11 | 7% |
Other | 25 | 17% |
Unknown | 14 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 43 | 29% |
Agricultural and Biological Sciences | 18 | 12% |
Physics and Astronomy | 17 | 11% |
Mathematics | 12 | 8% |
Engineering | 10 | 7% |
Other | 30 | 20% |
Unknown | 20 | 13% |