방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 다층 커뮤니티 탐지× | 확률적 블록 모형 (Stochastic Block Model, SBM)× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 2010–2014 | 1983 |
| 창시자≠ | Mucha, P. J. et al.; Kivela, M. et al. | — |
| 유형≠ | Community detection algorithm for multilayer networks | Probabilistic generative graph model |
| 원전≠ | 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 ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| 별칭 | multilayer clustering, multiplex community detection, cross-layer community detection, MCD | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| 관련≠ | 5 | 7 |
| 요약≠ | Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss. | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. |
| ScholarGate데이터셋 ↗ |
|
|