方法对比
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| 加权指数随机图模型× | 加权社会网络分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2012 | 2004–2010 |
| 提出者≠ | Krivitsky, P. N. | Barrat, A.; Opsahl, T. et al. |
| 类型≠ | Statistical network model | Network analysis framework |
| 开创性文献≠ | Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128. DOI ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| 别名 | W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model | Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis |
| 相关≠ | 4 | 6 |
| 摘要≠ | The Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce. | Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships. |
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