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| टेम्पोरल कम्युनिटी डिटेक्शन (Temporal Community Detection)× | सामाजिक नेटवर्क विश्लेषण× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2010 | 1934 (sociometry); 1994 (modern formalization) |
| प्रवर्तक≠ | Mucha, P. J. et al. | Moreno, J.L.; formalized by Wasserman & Faust |
| प्रकार≠ | Network clustering algorithm | Structural/relational analysis framework |
| 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 ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| उपनाम | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection | SNA, network analysis, sociometric analysis, relational analysis |
| 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. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
| ScholarGateडेटासेट ↗ |
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