Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Социальный сетевой анализ× | Собственная центральность× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1934 (sociometry); 1994 (modern formalization) | 1972 |
| Автор метода≠ | Moreno, J.L.; formalized by Wasserman & Faust | Bonacich, P. |
| Тип≠ | Structural/relational analysis framework | Centrality measure |
| Основополагающий источник≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Другие названия | SNA, network analysis, sociometric analysis, relational analysis | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. | 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 данных ↗ |
|
|