Title |
Entropy of Dynamical Social Networks
|
---|---|
Published in |
PLOS ONE, December 2011
|
DOI | 10.1371/journal.pone.0028116 |
Pubmed ID | |
Authors |
Kun Zhao, Márton Karsai, Ginestra Bianconi |
Abstract |
Human dynamical social networks encode information and are highly adaptive. To characterize the information encoded in the fast dynamics of social interactions, here we introduce the entropy of dynamical social networks. By analysing a large dataset of phone-call interactions we show evidence that the dynamical social network has an entropy that depends on the time of the day in a typical week-day. Moreover we show evidence for adaptability of human social behavior showing data on duration of phone-call interactions that significantly deviates from the statistics of duration of face-to-face interactions. This adaptability of behavior corresponds to a different information content of the dynamics of social human interactions. We quantify this information by the use of the entropy of dynamical networks on realistic models of social interactions. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 3 | 23% |
Korea, Republic of | 1 | 8% |
United Kingdom | 1 | 8% |
India | 1 | 8% |
Belgium | 1 | 8% |
Ireland | 1 | 8% |
Luxembourg | 1 | 8% |
United States | 1 | 8% |
Chile | 1 | 8% |
Other | 1 | 8% |
Unknown | 1 | 8% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 7 | 54% |
Members of the public | 6 | 46% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 6 | 4% |
Spain | 3 | 2% |
United States | 3 | 2% |
United Kingdom | 3 | 2% |
Mexico | 2 | 1% |
Italy | 2 | 1% |
Brazil | 1 | <1% |
Indonesia | 1 | <1% |
Germany | 1 | <1% |
Other | 4 | 3% |
Unknown | 119 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 39 | 27% |
Researcher | 30 | 21% |
Student > Master | 14 | 10% |
Professor | 12 | 8% |
Student > Doctoral Student | 10 | 7% |
Other | 27 | 19% |
Unknown | 13 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Physics and Astronomy | 34 | 23% |
Computer Science | 32 | 22% |
Social Sciences | 14 | 10% |
Mathematics | 10 | 7% |
Agricultural and Biological Sciences | 9 | 6% |
Other | 26 | 18% |
Unknown | 20 | 14% |