Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Umuhimu wa Eigenvector× | Ukaribu wa Kati (Closeness Centrality)× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1972 | 1950 (formalized 1979) |
| Mwanzilishi≠ | Bonacich, P. | Bavelas, A.; formalized by Freeman, L. C. |
| Aina≠ | Centrality measure | Node-level centrality index |
| Chanzo asilia≠ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Majina mbadala | eigenvector centrality, EC, Bonacich centrality, power centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | 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. | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. |
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