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| Centralitás-elemzés× | Térbeli Lag Modell (SAR / Autoregresszív Térbeli)× | |
|---|---|---|
| Tudományterület≠ | Hálózatelemzés | Térbeli elemzés |
| Módszercsalád≠ | Process / pipeline | Regression model |
| Keletkezés éve≠ | 1979 | 1988 |
| Megalkotó≠ | Linton C. Freeman | Anselin (textbook formalisation); LeSage & Pace |
| Típus≠ | Descriptive / exploratory network measure family | Spatial autoregressive regression |
| Alapmű≠ | Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239. DOI ↗ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| Alternatív nevek | Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | 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. | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. |
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