Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesovská detekce komunit× | Sociální síťová analýza× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2001–2014 | 1934 (sociometry); 1994 (modern formalization) |
| Tvůrce≠ | Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P. | Moreno, J.L.; formalized by Wasserman & Faust |
| Typ≠ | Probabilistic generative model / inference | Structural/relational analysis framework |
| Původní zdroj≠ | Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Další názvy | Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning | SNA, network analysis, sociometric analysis, relational analysis |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | Bayesian community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
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