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| 베이지안 사회 연결망 분석× | 확률적 블록 모형 (Stochastic Block Model, SBM)× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 2002 | 1983 |
| 창시자≠ | Hoff, P. D.; Raftery, A. E.; Handcock, M. S. | — |
| 유형≠ | Probabilistic / Bayesian network model | Probabilistic generative graph model |
| 원전≠ | Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| 별칭 | Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 관련≠ | 5 | 7 |
| 요약≠ | Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error. | 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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