Process / pipeline
指数随机图模型(ERGM / p*)
指数随机图模型(ERGM),也称为p*模型,是一种用于网络分析的统计框架,它将观测网络的概率建模为其局部结构特征(如互惠性、三角形和度分布)的函数。ERGM源于Frank和Strauss(1986)的基础性工作,并由Wasserman和Pattison(1996)以及Robins等人(2007)扩展为现代框架,是社会网络分析的推断标准,能够检验观测到的网络结构是偶然产生还是反映了真实的社会过程。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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 ↗
- Lusher, D., Koskinen, J., & Robins, G. (Eds.) (2012). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press. ISBN: 9780521193566
如何引用本页
ScholarGate. (2026, June 1). Exponential Random Graph Model (ERGM / p*). ScholarGate. https://scholargate.app/zh/network-analysis/exponential-random-graph
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 因果发现算法 (PC, FCI, LiNGAM)因果推断↔ compare
- 社群检测网络分析↔ compare
- DBSCAN机器学习↔ compare
- 图注意力网络深度学习↔ compare
- 图神经网络深度学习↔ compare
- 文本网络分析文本挖掘↔ compare