পদ্ধতির তুলনা করুন

	ওয়েটেড স্টোকাস্টিক ব্লক মডেল	স্টোকাস্টিক ব্লক মডেল
ক্ষেত্র	নেটওয়ার্ক বিশ্লেষণ	নেটওয়ার্ক বিশ্লেষণ
পরিবার	Machine learning	Process / pipeline
উদ্ভবের বছর	2014	1983
প্রবর্তক	Aicher, C.; Jacobs, A. Z.; Clauset, A.	—
ধরন	Generative probabilistic model	Probabilistic generative graph model
মৌলিক উৎস	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.
অপর নাম	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
সম্পর্কিত	6	7
সারসংক্ষেপ	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.