विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बायेसियन एक्सपोनेंशियल रैंडम ग्राफ़ मॉडल× | स्टोकेस्टिक ब्लॉक मॉडल× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार≠ | Machine learning | Process / pipeline |
| उद्भव वर्ष≠ | 2011 | 1983 |
| प्रवर्तक≠ | Caimo, A., & Friel, N. | — |
| प्रकार≠ | Bayesian statistical model for networks | Probabilistic generative graph model |
| मौलिक स्रोत≠ | Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55. DOI ↗ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ |
| उपनाम | Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) |
| संबंधित≠ | 4 | 7 |
| सारांश≠ | The Bayesian Exponential Random Graph Model (Bayesian ERGM or BERGM) extends the classical ERGM framework by placing prior distributions over the model parameters and using Markov chain Monte Carlo methods to obtain full posterior distributions. Introduced by Caimo and Friel (2011), it allows researchers to quantify parameter uncertainty and incorporate prior knowledge when modelling the structural features of social and other complex networks. | 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. |
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