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| Model Blok Stokastik× | DBSCAN× | Jaringan Perhatian Graf× | |
|---|---|---|---|
| Bidang≠ | Analisis Jaringan | Pembelajaran Mesin | Pembelajaran Mendalam |
| Keluarga≠ | Process / pipeline | Machine learning | Machine learning |
| Tahun asal≠ | 1983 | 1996 | 2018 |
| Pencetus≠ | — | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Veličković, P. et al. |
| Tipe≠ | Probabilistic generative graph model | Density-based clustering algorithm | Graph neural network (attention-based) |
| Sumber perintis≠ | 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 ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| Alias≠ | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| Terkait≠ | 7 | 3 | 4 |
| Ringkasan≠ | 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. | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). |
| ScholarGateSet data ↗ |
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