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| Kekpusatan Kedekatan Dinamik× | Analisis Jaringan Sosial Temporal× | |
|---|---|---|
| Bidang | Analisis Rangkaian | Analisis Rangkaian |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2010–2012 | 2000s–2010s |
| Pengasas≠ | Tang, J. et al.; Holme, P. & Saramäki, J. | Moody, J.; Holme, P.; Saramäki, J. |
| Jenis≠ | Centrality measure for temporal networks | Longitudinal network analysis |
| Sumber perintis≠ | Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Alias | temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Berkaitan≠ | 5 | 4 |
| Ringkasan≠ | Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time. | Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time. |
| ScholarGateSet data ↗ |
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