Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Ohjattu eigenvector-keskeisyys× | Suunnattu läheisyyskeskeisyys× | |
|---|---|---|
| Tieteenala | Verkostoanalyysi | Verkostoanalyysi |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1972–1987 | 1979–1994 |
| Kehittäjä≠ | Bonacich, P. | Freeman, L. C.; Wasserman, S. & Faust, K. |
| Tyyppi≠ | Centrality measure (eigenvector-based, directed) | Centrality measure |
| Alkuperäislähde≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4 |
| Rinnakkaisnimet | directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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 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. |
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