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| टेम्पोरल आइगेनवेक्टर सेंट्रैलिटी (Temporal Eigenvector Centrality)× | कालिक डिग्री केन्द्रीयता× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2011-2017 | 2011–2012 |
| प्रवर्तक≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. |
| प्रकार≠ | Centrality measure for temporal networks | Centrality measure (temporal extension) |
| मौलिक स्रोत≠ | Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. DOI ↗ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| उपनाम | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC |
| संबंधित≠ | 5 | 6 |
| सारांश≠ | Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network. | Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window. |
| ScholarGateडेटासेट ↗ |
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