Machine learningNetwork science
加权指数随机图模型
加权指数随机图模型（W-ERGM）将经典的二元ERGM框架扩展到具有量化值的边的网络——例如，联系频率、贸易量或合作强度。它将整个有权边网络建模为在所有可能的加权图上定义的概率分布，使研究人员能够检验诸如互惠性、传递性或度分布等结构模式是否超出了偶然性所能产生的范围。
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来源
- Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128. DOI: 10.1214/12-EJS696 ↗
- Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173–191. DOI: 10.1016/j.socnet.2006.08.002 ↗
如何引用本页
ScholarGate. (2026, June 3). Weighted Exponential Random Graph Model (Valued-Edge ERGM). ScholarGate. https://scholargate.app/zh/network-analysis/weighted-exponential-random-graph-model
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