Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Анализ на его мрежи× | Анализ на централност× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1992 (Burt); foundational measurement formalised by Marsden 2002 | 1979 |
| Създател≠ | Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures) | Linton C. Freeman |
| Тип≠ | Descriptive / relational network analysis | Descriptive / exploratory network measure family |
| Основополагащ източник≠ | Burt, R.S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press. ISBN: 9780674843714 | Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239. DOI ↗ |
| Други названия≠ | personal network analysis, egocentric network analysis, Ego Ağı Analizi (Personal Network Analysis) | Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality |
| Свързани≠ | 6 | 5 |
| Резюме≠ | Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces ego-level indicators such as network size, density, constraint, and brokerage role that reveal how each individual's social position shapes their access to information, resources, and influence. | Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors. |
| ScholarGateНабор от данни ↗ |
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