Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Centralitatea eigenvector direcționată× | Centralitatea vectorului propriu× | |
|---|---|---|
| Domeniu | Analiza rețelelor | Analiza rețelelor |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1972–1987 | 1972 |
| Autorul original | Bonacich, P. | Bonacich, P. |
| Tip≠ | Centrality measure (eigenvector-based, directed) | Centrality measure |
| Sursa seminală≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Denumiri alternative | directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Înrudite≠ | 5 | 6 |
| Rezumat≠ | 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. | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. |
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