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| Gerichtete Eigenvektorzentralität× | Gerichtetes PageRank× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1972–1987 | 1998 |
| Urheber≠ | Bonacich, P. | Brin, S. & Page, L. |
| Typ≠ | Centrality measure (eigenvector-based, directed) | Iterative authority-scoring algorithm |
| Wegweisende Quelle≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | 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 EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality | PageRank, PR, Google PageRank, directed link analysis |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Directed eigenvector centrality extends the classic eigenvector centrality to directed graphs by scoring each node according to the centrality of the nodes that point to it (in-direction) or that it points to (out-direction). A node earns a high score not merely by having many connections but by being connected to other highly central nodes, capturing asymmetric influence in citation networks, social hierarchies, and information flows. | 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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