विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| गतिशील स्टोकेस्टिक ब्लॉक मॉडल× | मॉड्यूलरिटी विश्लेषण× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2011 | 2004 |
| प्रवर्तक≠ | Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R. | Newman, M. E. J. & Girvan, M. |
| प्रकार≠ | Generative probabilistic model | Community detection / graph partitioning |
| मौलिक स्रोत≠ | Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| उपनाम | DSBM, dynamic SBM, time-varying stochastic block model, temporal block model | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| संबंधित | 5 | 5 |
| सारांश≠ | The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
| ScholarGateडेटासेट ↗ |
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