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| Anàlisi Bayesiana de Xarxes Ego× | Model Estocàstic de Blocs Bayesà (Bayesian SBM)× | |
|---|---|---|
| Camp | Anàlisi de xarxes | Anàlisi de xarxes |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2010s | 2001–2014 |
| Autor original≠ | Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors) | Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P. |
| Tipus≠ | Probabilistic network model | Probabilistic generative model with Bayesian inference |
| Font seminal≠ | Krivitsky, P. N., & Kolaczyk, E. D. (2015). On the question of effective sample size in network modeling: An asymptotic inquiry. Statistical Science, 30(2), 184–198. DOI ↗ | Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804. DOI ↗ |
| Àlies | Bayesian personal network analysis, Bayesian egocentric network analysis, probabilistic ego network modeling, Bayesian egonet | Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model |
| Relacionats | 5 | 5 |
| Resum≠ | Bayesian ego network analysis applies probabilistic inference to ego-centered (personal) network data, combining a likelihood model for the ego's local network with prior distributions over network parameters. The result is a full posterior distribution that quantifies uncertainty about structural features such as alter composition, tie density, and network size — rather than producing point estimates alone. | The Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches. |
| ScholarGateConjunt de dades ↗ |
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