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| Analisis Jaringan Multiplex Temporal× | Analisis Jaringan Sosial× | |
|---|---|---|
| Bidang | Analisis Jaringan | Analisis Jaringan |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2012–2014 | 1934 (sociometry); 1994 (modern formalization) |
| Pencetus≠ | Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors) | Moreno, J.L.; formalized by Wasserman & Faust |
| Tipe≠ | Structural and dynamic network analysis | Structural/relational analysis framework |
| Sumber perintis≠ | 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 ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Alias | TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis | SNA, network analysis, sociometric analysis, relational analysis |
| Terkait | 5 | 5 |
| Ringkasan≠ | Temporal multiplex network analysis studies relational systems in which actors are connected by multiple distinct types of relationships that all evolve over time. By simultaneously tracking layer heterogeneity and temporal dynamics, the method reveals how different interaction channels co-evolve, which actors hold persistent cross-layer influence, and how structural changes propagate across relationship types and time periods. | 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. |
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