방법 비교

	가중치 확률 블록 모델	확률적 블록 모형 (Stochastic Block Model, SBM)
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Process / pipeline
기원 연도	2014	1983
창시자	Aicher, C.; Jacobs, A. Z.; Clauset, A.	—
유형	Generative probabilistic model	Probabilistic generative graph model
원전	Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
별칭	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
관련	6	7
요약	The Weighted Stochastic Block Model (W-SBM) extends the classical stochastic block model to networks whose edges carry numerical weights. By positing that edge weights between node pairs arise from distributions that depend on the block memberships of those nodes, it simultaneously infers a partition of nodes into communities and a set of block-to-block weight parameters — recovering structure invisible to unweighted methods.	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.