方法对比
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| 时序多重网络分析× | 动态社群侦测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2012–2014 | 2010 (key formalization); earlier work 2002–2009 |
| 提出者≠ | Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors) | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) |
| 类型≠ | Structural and dynamic network analysis | Graph clustering / community discovery |
| 开创性文献≠ | 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 ↗ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ |
| 别名 | TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis | DCD, temporal community detection, evolving community detection, dynamic graph clustering |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research. |
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