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| Gerichtete Nähe-Zentralität× | Gerichtetes PageRank× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1979–1994 | 1998 |
| Urheber≠ | Freeman, L. C.; Wasserman, S. & Faust, K. | Brin, S. & Page, L. |
| Typ≠ | Centrality measure | Iterative authority-scoring algorithm |
| Wegweisende Quelle≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4 | Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th International Conference on World Wide Web (WWW7), 107–117. Elsevier. link ↗ |
| Aliasnamen | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness | PageRank, PR, Google PageRank, directed link analysis |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Directed closeness centrality extends the classical closeness measure to directed networks by separately quantifying how quickly a node can be reached by others (in-closeness) and how quickly it can reach all others (out-closeness). It is a foundational node-level metric in social network analysis and graph theory, used wherever link direction conveys meaningful asymmetry such as citation flows, information cascades, or authority hierarchies. | Directed PageRank is a link-based authority scoring algorithm that assigns importance scores to nodes in a directed graph by iteratively redistributing rank through outgoing edges. Introduced by Brin and Page in 1998 as the backbone of Google Search, it measures not just how many in-links a node has but how authoritative the nodes pointing to it are. |
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