Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beiziešu nejaušo grafu modelis (Bayesian Exponential Random Graph Model)× | Bayesiešu stohastiskais bloku modelis× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2011 | 2001–2014 |
| Autors≠ | Caimo, A., & Friel, N. | Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P. |
| Tips≠ | Bayesian statistical model for networks | Probabilistic generative model with Bayesian inference |
| Pirmavots≠ | Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. 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 ↗ |
| Citi nosaukumi | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM | Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The Bayesian Exponential Random Graph Model (Bayesian ERGM or BERGM) extends the classical ERGM framework by placing prior distributions over the model parameters and using Markov chain Monte Carlo methods to obtain full posterior distributions. Introduced by Caimo and Friel (2011), it allows researchers to quantify parameter uncertainty and incorporate prior knowledge when modelling the structural features of social and other complex networks. | 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. |
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