विधियों की तुलना करें

	स्टोकेस्टिक ब्लॉक मॉडल	प्रिंसिपल कंपोनेंट एनालिसिस
क्षेत्र	नेटवर्क विश्लेषण	मशीन अधिगम
परिवार	Process / pipeline	Machine learning
उद्भव वर्ष	1983	2002
प्रवर्तक	—	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
प्रकार	Probabilistic generative graph model	Unsupervised dimensionality reduction
मौलिक स्रोत	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
उपनाम	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
संबंधित	7	3
सारांश	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.