Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1371/journal.pone.0028116 | PLOS ONE |
Keywords | Field | DocType |
chemistry,probability,information content,medicine,social interaction,biology,communication,interpersonal relations,human interaction,physics,engineering,entropy,social behavior | Social relation,Adaptability,ENCODE,Social network,Interpersonal relationship,Theoretical computer science,Social dynamics,Bioinformatics,Social support,Physics | Journal |
Volume | Issue | ISSN |
6 | 12 | 1932-6203 |
Citations | PageRank | References |
8 | 0.72 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Zhao | 1 | 56 | 5.24 |
| Márton Karsai | 2 | 422 | 30.42 |
| Ginestra Bianconi | 3 | 189 | 16.94 |