विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| टेम्पोरल कम्युनिटी डिटेक्शन (Temporal Community Detection)× | मॉड्यूलरिटी विश्लेषण× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2010 | 2004 |
| प्रवर्तक≠ | Mucha, P. J. et al. | Newman, M. E. J. & Girvan, M. |
| प्रकार≠ | Network clustering algorithm | Community detection / graph partitioning |
| मौलिक स्रोत≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| उपनाम | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| संबंधित≠ | 6 | 5 |
| सारांश≠ | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. | 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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