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| Sentralitas Derajat Dinamis× | Analisis Jaringan Sosial Temporal× | |
|---|---|---|
| Bidang | Analisis Jaringan | Analisis Jaringan |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2012 | 2000s–2010s |
| Pencetus≠ | Holme, P. & Saramaki, J.; Kim, H. & Anderson, R. | Moody, J.; Holme, P.; Saramäki, J. |
| Tipe≠ | Centrality measure (temporal extension) | Longitudinal network analysis |
| Sumber perintis≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Alias | time-varying degree centrality, temporal degree centrality, evolving degree centrality, DDC | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Terkait≠ | 5 | 4 |
| Ringkasan≠ | Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network. | 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. |
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