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| 다층 네트워크 확산 분석× | 사회 연결망 분석× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2013–2014 | 1934 (sociometry); 1994 (modern formalization) |
| 창시자≠ | Gomez, S. et al.; Boccaletti, S. et al. | Moreno, J.L.; formalized by Wasserman & Faust |
| 유형≠ | Network diffusion model | Structural/relational analysis framework |
| 원전≠ | Gomez, S., Diaz-Guilera, A., Gomez-Gardenes, J., Perez-Vicente, C. J., Moreno, Y., & Arenas, A. (2013). Diffusion dynamics on multiplex networks. Physical Review Letters, 110(2), 028701. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| 별칭 | multiplex diffusion analysis, multilayer spreading analysis, cross-layer contagion analysis, diffusion on multiplex networks | SNA, network analysis, sociometric analysis, relational analysis |
| 관련≠ | 6 | 5 |
| 요약≠ | Multilayer Network Diffusion Analysis models how information, disease, or influence spreads across a system composed of multiple, interconnected network layers. By coupling diffusion processes across layers — for instance social ties, transport routes, and online channels simultaneously — it reveals how cross-layer interactions accelerate or dampen spreading and lowers epidemic thresholds compared to single-layer models. | 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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