Machine learningNetwork science

Weighted Eigenvector Centrality

Weighted eigenvector centrality extends the classic eigenvector centrality measure to graphs where edges carry numerical weights, scoring each node proportionally to the sum of its neighbors' scores multiplied by the connecting edge weights. Nodes score highly not just by having many connections but by being strongly linked to other influential nodes, making the measure sensitive to both tie strength and network position simultaneously.

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แหล่งอ้างอิง

  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

วิธีอ้างอิงหน้านี้

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

Which method?

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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ถูกอ้างอิงโดย

ScholarGateWeighted Eigenvector Centrality (Weighted Eigenvector Centrality (Spectral Prestige in Weighted Networks)). สืบค้นเมื่อ 2026-06-15 จาก https://scholargate.app/th/network-analysis/weighted-eigenvector-centrality · ชุดข้อมูล: https://doi.org/10.5281/zenodo.20539026