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| Mô hình khối ngẫu nhiên có trọng số× | Mô hình Khối Ngẫu nhiên (Stochastic Block Model - SBM)× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ≠ | Machine learning | Process / pipeline |
| Năm ra đời≠ | 2014 | 1983 |
| Người khởi xướng≠ | Aicher, C.; Jacobs, A. Z.; Clauset, A. | — |
| Loại≠ | Generative probabilistic model | Probabilistic generative graph model |
| Công trình gốc≠ | Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Tên gọi khác | W-SBM, weighted SBM, weighted block model, weighted community detection via SBM | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Liên quan≠ | 6 | 7 |
| Tóm tắt≠ | The Weighted Stochastic Block Model (W-SBM) extends the classical stochastic block model to networks whose edges carry numerical weights. By positing that edge weights between node pairs arise from distributions that depend on the block memberships of those nodes, it simultaneously infers a partition of nodes into communities and a set of block-to-block weight parameters — recovering structure invisible to unweighted methods. | 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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