方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 加权特征向量中心性× | 加权PageRank× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1987 (binary); 2010 (weighted generalization) | 2004 |
| 提出者≠ | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) | Xing, W. & Ghorbani, A. |
| 类型≠ | Spectral centrality measure | Centrality measure / ranking algorithm |
| 开创性文献≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Xing, W., & Ghorbani, A. (2004). Weighted PageRank algorithm. Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR '04), pp. 305–314. IEEE. DOI ↗ |
| 别名 | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige | WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank |
| 相关 | 6 | 6 |
| 摘要≠ | 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 PageRank extends the classic PageRank algorithm to networks where edges carry different strengths or frequencies, distributing importance proportionally to both incoming and outgoing edge weights rather than treating all links equally. This makes it substantially more informative than binary PageRank in any network where connection strength matters. |
| ScholarGate数据集 ↗ |
|
|