Machine learningNetwork science

Težinska sopstvena centralnost

Težinska sopstvena centralnost proširuje klasičnu meru sopstvene centralnosti na grafove gde ivice nose numeričke težine, ocenjujući svaki čvor proporcionalno zbiru rezultata njegovih suseda pomnoženih težinama povezujućih ivica. Čvorovi postižu visoke rezultate ne samo imajući mnogo veza, već i snažnim povezivanjem sa drugim uticajnim čvorovima, čineći meru istovremeno osetljivom na jačinu veze i poziciju u mreži.

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Izvori

  1. Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI: 10.1086/228631
  2. Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI: 10.1016/j.socnet.2010.03.006

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Weighted Eigenvector Centrality (Spectral Prestige in Weighted Networks). ScholarGate. https://scholargate.app/sr/network-analysis/weighted-eigenvector-centrality

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Citirana u

ScholarGateWeighted Eigenvector Centrality (Weighted Eigenvector Centrality (Spectral Prestige in Weighted Networks)). Preuzeto 2026-06-15 sa https://scholargate.app/sr/network-analysis/weighted-eigenvector-centrality · Skup podataka: https://doi.org/10.5281/zenodo.20539026