เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Weighted Eigenvector Centrality× | ค่ากลางแบบวัดความใกล้ชิดถ่วงน้ำหนัก× | |
|---|---|---|
| สาขาวิชา | การวิเคราะห์เครือข่าย | การวิเคราะห์เครือข่าย |
| ตระกูล | Machine learning | Machine learning |
| ปีกำเนิด≠ | 1987 (binary); 2010 (weighted generalization) | 2010 |
| ผู้ริเริ่ม≠ | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) | Opsahl, T.; Agneessens, F.; Skvoretz, J. |
| ประเภท≠ | Spectral centrality measure | Centrality measure (network analysis) |
| แหล่งต้นตำรับ≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ |
| ชื่อเรียกอื่น | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness |
| ที่เกี่ยวข้อง | 6 | 6 |
| สรุป≠ | 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. | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. |
| ScholarGateชุดข้อมูล ↗ |
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