方法对比
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| 随机块模型× | 文本网络分析× | |
|---|---|---|
| 领域≠ | 网络分析 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1983 | 2011 (Paranyushkin); 2005 (Diesner & Carley) |
| 提出者≠ | — | Dmitry Paranyushkin; Jana Diesner & Kathleen M. Carley |
| 类型≠ | Probabilistic generative graph model | Text-mining network method |
| 开创性文献≠ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ | Paranyushkin, D. (2011). Identifying the Pathways for Meaning Circulation Using Text Network Analysis. Nodus Labs. link ↗ |
| 别名≠ | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | semantic network analysis, word co-occurrence network, Metin Ağ Analizi (Text Network Analysis) |
| 相关≠ | 7 | 4 |
| 摘要≠ | 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. | Text network analysis models the words or concepts in a text as nodes and their co-occurrences as edges, then uses network metrics to reveal the structure of meaning. The approach was advanced by Diesner and Carley (2005) for communication networks and by Paranyushkin (2011) for tracing the pathways of meaning circulation in text. |
| ScholarGate数据集 ↗ |
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