Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Аналіз дифузії у зважених мережах× | Виявлення зважених спільнот× | |
|---|---|---|
| Галузь | Мережевий аналіз | Мережевий аналіз |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2004 | 2004–2008 |
| Автор методу≠ | Barrat, A.; Newman, M. E. J. | Newman, M. E. J.; Blondel et al. |
| Тип≠ | Network diffusion model | Graph clustering / community detection |
| Основоположне джерело≠ | 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 ↗ | Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. DOI ↗ |
| Інші назви | WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD |
| Пов'язані | 6 | 6 |
| Підсумок≠ | 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. | Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation. |
| ScholarGateНабір даних ↗ |
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