เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| ค่ากลางแบบวัดความใกล้ชิดถ่วงน้ำหนัก× | Weighted Eigenvector Centrality× | |
|---|---|---|
| สาขาวิชา | การวิเคราะห์เครือข่าย | การวิเคราะห์เครือข่าย |
| ตระกูล | Machine learning | Machine learning |
| ปีกำเนิด≠ | 2010 | 1987 (binary); 2010 (weighted generalization) |
| ผู้ริเริ่ม≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) |
| ประเภท≠ | Centrality measure (network analysis) | Spectral centrality measure |
| แหล่งต้นตำรับ≠ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ |
| ชื่อเรียกอื่น | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige |
| ที่เกี่ยวข้อง | 6 | 6 |
| สรุป≠ | 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. | 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. |
| ScholarGateชุดข้อมูล ↗ |
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