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| Gewichtete Ego-Netzwerkanalyse× | Gradzentralität× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1954–2002 | 1978 |
| Urheber≠ | Barnes, J. A.; Bott, E.; Marsden, P. V. | Freeman, L. C. |
| Typ≠ | Ego-centered network analysis with weighted ties | Node-level centrality measure |
| Wegweisende Quelle≠ | Marsden, P. V. (2002). Egocentric and sociocentric measures of network centrality. Social Networks, 24(4), 407–422. DOI ↗ | Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Aliasnamen | weighted personal network analysis, ego-centered weighted network analysis, weighted egonet analysis, tie-strength ego network analysis | node degree, degree score, DC, connectivity centrality |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Weighted ego network analysis examines the personal network of a focal actor (the ego) and incorporates tie strength — measured as interaction frequency, closeness, or resource exchange — as edge weights. By moving beyond simple presence or absence of a tie, it captures how much each relationship matters and how those varying strengths shape outcomes such as social support, information access, or influence. | Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis. |
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