Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Векторна централност във времето× | Собствена централност (Eigenvector Centrality)× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2011-2017 | 1972 |
| Създател≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Bonacich, P. |
| Тип≠ | Centrality measure for temporal networks | Centrality measure |
| Основополагащ източник≠ | Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. DOI ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Други названия | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Свързани≠ | 5 | 6 |
| Резюме≠ | Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network. | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. |
| ScholarGateНабор от данни ↗ |
|
|