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| 베이지안 사회 연결망 분석× | 다층 사회 연결망 분석× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2002 | 2014 |
| 창시자≠ | Hoff, P. D.; Raftery, A. E.; Handcock, M. S. | Kivela, M.; Boccaletti, S. et al. |
| 유형≠ | Probabilistic / Bayesian network model | Structural network analysis framework |
| 원전≠ | Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098. DOI ↗ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ |
| 별칭 | Bayesian SNA, Bayesian network modeling, probabilistic social network analysis, Bayesian relational modeling | MSNA, multiplex network analysis, multilayer network analysis, interconnected network analysis |
| 관련≠ | 5 | 6 |
| 요약≠ | Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error. | Multilayer social network analysis extends classical single-layer network methods to settings where actors are connected through multiple, distinct types of ties — such as friendship, professional collaboration, and online interaction — simultaneously. By modeling each type of relationship as a separate layer and explicitly representing connections across layers, it captures structural complexity that a single aggregated network would hide. |
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