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| Бейсовско откриване на общности× | Социален мрежов анализ× | |
|---|---|---|
| Област | Мрежови анализ | Мрежови анализ |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2001–2014 | 1934 (sociometry); 1994 (modern formalization) |
| Създател≠ | Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P. | Moreno, J.L.; formalized by Wasserman & Faust |
| Тип≠ | Probabilistic generative model / inference | Structural/relational analysis framework |
| Основополагащ източник≠ | 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 |
| Други названия | Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning | SNA, network analysis, sociometric analysis, relational analysis |
| Свързани | 5 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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