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| Dynamische Community-Erkennung× | Stochastic Block Model× | |
|---|---|---|
| Fachgebiet | Netzwerkanalyse | Netzwerkanalyse |
| Familie≠ | Machine learning | Process / pipeline |
| Entstehungsjahr≠ | 2010 (key formalization); earlier work 2002–2009 | 1983 |
| Urheber≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | — |
| Typ≠ | Graph clustering / community discovery | Probabilistic generative graph model |
| Wegweisende Quelle≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| Aliasnamen | DCD, temporal community detection, evolving community detection, dynamic graph clustering | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| Verwandt≠ | 5 | 7 |
| Zusammenfassung≠ | Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research. | 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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