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| 有向模块度分析× | 随机块模型× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2008 | 1983 |
| 提出者≠ | Leicht, E. A. & Newman, M. E. J. | — |
| 类型≠ | Community detection / graph partitioning | Probabilistic generative graph model |
| 开创性文献≠ | Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| 别名 | directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 相关≠ | 5 | 7 |
| 摘要≠ | Directed modularity analysis extends the classic Newman-Girvan modularity framework to directed graphs, where edges carry a source and a destination. Formalized by Leicht and Newman in 2008, it partitions nodes into communities by maximizing a modularity score that accounts for each node's separate in-degree and out-degree in the null model, making it the standard approach for community detection in citation networks, information flows, and other asymmetric relational data. | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. |
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