Name
Abstract | ||
|---|---|---|
The bidirectional selection between two classes widely emerges in various social lives, such as commercial trading and mate choosing. Until now, the discussions on bidirectional selection in structured human society are quite limited. We demonstrated theoretically that the rate of successfully matching is affected greatly by individuals' neighborhoods in social networks, regardless of the type of networks. Furthermore, it is found that the high average degree of networks contributes to increasing rates of successful matches. The matching performance in different types of networks has been quantitatively investigated, revealing that the small-world networks reinforces the matching rate more than scale-free networks at given average degree. In addition, our analysis is consistent with the modeling result, which provides the theoretical understanding of underlying mechanisms of matching in complex networks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1038/srep07577 | SCIENTIFIC REPORTS |
Keywords | Field | DocType |
bioinformatics,biomedical research | Social network,Computer science,Human dynamics,Complex network,Artificial intelligence,Social support | Journal |
Volume | ISSN | Citations |
4 | 2045-2322 | 4 |
PageRank | References | Authors |
0.51 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Zhou | 1 | 4 | 0.51 |
| Zhe He | 2 | 6 | 3.61 |
| Luo-luo Jiang | 3 | 99 | 9.33 |
| Nian-Xin Wang | 4 | 4 | 0.85 |
| Bing-Hong Wang | 5 | 4 | 0.51 |