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| Analisis Jaringan Sosial Temporal× | Analisis Jaringan Multiplex× | |
|---|---|---|
| Bidang | Analisis Jaringan | Analisis Jaringan |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2000s–2010s | 2014 |
| Pencetus≠ | Moody, J.; Holme, P.; Saramäki, J. | Kivela, M.; Boccaletti, S. et al. |
| Tipe≠ | Longitudinal network analysis | Structural network model |
| Sumber perintis≠ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ |
| Alias | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Terkait≠ | 4 | 6 |
| Ringkasan≠ | 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. | Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities. |
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