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| Stochastic Block Model× | DBSCAN× | |
|---|---|---|
| Fachgebiet≠ | Netzwerkanalyse | Maschinelles Lernen |
| Familie≠ | Process / pipeline | Machine learning |
| Entstehungsjahr≠ | 1983 | 1996 |
| Urheber≠ | — | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Typ≠ | Probabilistic generative graph model | Density-based clustering algorithm |
| Wegweisende Quelle≠ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ |
| Aliasnamen≠ | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Verwandt≠ | 7 | 3 |
| Zusammenfassung≠ | 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. | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. |
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