विधियों की तुलना करें

	स्टोकेस्टिक ब्लॉक मॉडल	ग्राफ़ न्यूरल नेटवर्क
क्षेत्र	नेटवर्क विश्लेषण	गहन अधिगम
परिवार	Process / pipeline	Machine learning
उद्भव वर्ष	1983	2017
प्रवर्तक	—	Kipf, T.N. & Welling, M.
प्रकार	Probabilistic generative graph model	Deep learning on graph-structured data
मौलिक स्रोत	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.
उपनाम	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network
संबंधित	7	4
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.	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.