手法を比較

	多層コミュニティ検出	確率的ブロックモデル
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Process / pipeline
提唱年	2010–2014	1983
提唱者	Mucha, P. J. et al.; Kivela, M. et al.	—
種類	Community detection algorithm for multilayer networks	Probabilistic generative graph model
原典	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.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
別名	multilayer clustering, multiplex community detection, cross-layer community detection, MCD	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
関連	5	7
概要	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.