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| 가중 다중망 분석× | 가중 네트워크 확산 분석× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2014 | 2004 |
| 창시자≠ | Battiston, F.; Kivela, M. et al. | Barrat, A.; Newman, M. E. J. |
| 유형≠ | Network analysis framework | Network diffusion model |
| 원전≠ | Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804. DOI ↗ | Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ |
| 별칭 | WMNA, weighted multilayer network analysis, weighted multi-relational network analysis, multiplex weighted graph analysis | WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion |
| 관련≠ | 5 | 6 |
| 요약≠ | Weighted multiplex network analysis studies systems in which the same set of actors are connected through multiple types of relationships simultaneously, and each relationship carries a quantitative strength or frequency. By capturing both the variety and the intensity of ties across layers, it reveals patterns invisible to single-layer or unweighted network approaches. | Weighted Network Diffusion Analysis models how information, influence, disease, or resources spread through a network whose edges carry quantitative strength values. By letting tie weights govern transition probabilities, the method produces more realistic spreading dynamics than binary-edge diffusion, revealing which high-traffic pathways dominate propagation in social, biological, and information networks. |
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