Name
Abstract | ||
|---|---|---|
The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1371/journal.pone.0020648 | PLOS ONE |
Keywords | Field | DocType |
resource sharing,information retrieval,scale free,user experience,network model,recommender system,social network | Data science,Recommender system,User experience design,Social network,Biology,Rumor,Scale-free network,Bioinformatics,Social support,Empirical research,The Internet | Journal |
Volume | Issue | ISSN |
6 | 7 | 1932-6203 |
Citations | PageRank | References |
20 | 1.02 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zhou | 1 | 2744 | 152.77 |
| Matus Medo | 2 | 263 | 21.28 |
| Giulio Cimini | 3 | 126 | 13.77 |
| Zi-Ke Zhang | 4 | 403 | 28.84 |
| Yi-Cheng Zhang | 5 | 451 | 25.98 |