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| 베이즈 확률적 블록 모델× | 모듈성 분석× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001–2014 | 2004 |
| 창시자≠ | Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P. | Newman, M. E. J. & Girvan, M. |
| 유형≠ | Probabilistic generative model with Bayesian inference | Community detection / graph partitioning |
| 원전≠ | Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| 별칭 | Bayesian SBM, B-SBM, probabilistic block model, Bayesian community detection model | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
| ScholarGate데이터셋 ↗ |
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