Name
Abstract | ||
|---|---|---|
Temporal social networks are characterized by {heterogeneous} duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-36461-7_5 | arXiv: Physics and Society |
Field | DocType | Volume |
Social network,Computer science,Interfacing,Human dynamics,Weibull distribution,Theoretical computer science,Information and Communications Technology,Artificial intelligence,Social dynamics,Mobile phone,Machine learning | Journal | abs/1307.5675 |
Citations | PageRank | References |
3 | 0.39 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Zhao | 1 | 56 | 5.24 |
| Márton Karsai | 2 | 422 | 30.42 |
| Ginestra Bianconi | 3 | 189 | 16.94 |