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| 有向指数随机图模型× | 有向社区检测× | |
|---|---|---|
| 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) | 2008 |
| 提出者≠ | Frank, O. & Strauss, D.; extended by Robins, Pattison, Kalish & Lusher | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. |
| 类型≠ | Statistical generative model for directed networks | Graph partitioning / modularity optimization |
| 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 ↗ | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ |
| 别名 | Directed ERGM, p-star model (directed), directed p* model, directed Markov graph model | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning |
| 相关≠ | 4 | 6 |
| 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 community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways. |
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