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| Súlyozott sztochasztikus blokkmintázat× | Súlyozott társadalmi hálózatok elemzése× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2014 | 2004–2010 |
| Megalkotó≠ | Aicher, C.; Jacobs, A. Z.; Clauset, A. | Barrat, A.; Opsahl, T. et al. |
| Típus≠ | Generative probabilistic model | Network analysis framework |
| Alapmű≠ | Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248. DOI ↗ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| Alternatív nevek | W-SBM, weighted SBM, weighted block model, weighted community detection via SBM | Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | 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. | Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships. |
| ScholarGateAdatkészlet ↗ |
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