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| Gradzentralität× | Analyse sozialer Netzwerke× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1978 | 1934 (sociometry); 1994 (modern formalization) |
| Urheber≠ | Freeman, L. C. | Moreno, J.L.; formalized by Wasserman & Faust |
| Typ≠ | Node-level centrality measure | Structural/relational analysis framework |
| Wegweisende Quelle≠ | Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Aliasnamen | node degree, degree score, DC, connectivity centrality | SNA, network analysis, sociometric analysis, relational analysis |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. | 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. |
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