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| 有向指数随机图模型× | 定向社交网络分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1986 (foundations); 2007 (modern directed ERGM formulation) | 1994 |
| 提出者≠ | Frank, O. & Strauss, D.; extended by Robins, Pattison, Kalish & Lusher | Wasserman, S. & Faust, K. |
| 类型≠ | Statistical generative model for directed networks | Structural analysis of directed graphs |
| 开创性文献≠ | 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 ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| 别名 | Directed ERGM, p-star model (directed), directed p* model, directed Markov graph model | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis |
| 相关≠ | 4 | 5 |
| 摘要≠ | The Directed Exponential Random Graph Model (Directed ERGM) is a family of statistical models for directed networks that estimates the probability of observing a given directed graph as a function of structural configurations — such as reciprocity, transitive triads, and in-degree centralization — and node or dyad covariates, enabling principled inference about the social processes that generate directed ties. | Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades. |
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