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| 定向网络扩散分析× | 时间网络扩散分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927 | 2012 |
| 提出者≠ | Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading) | Holme, P. & Saramäki, J. |
| 类型≠ | Network spreading and cascade analysis | Network analysis framework |
| 开创性文献≠ | Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137–146. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| 别名 | directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation | TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks |
| 相关≠ | 6 | 5 |
| 摘要≠ | Directed network diffusion analysis studies how information, disease, behavior, or influence spreads through a network in which edges carry direction — meaning transmission flows one way along each link. It combines graph-theoretic representations with stochastic spreading models such as independent cascade, linear threshold, or SIR/SIS, and is central to influence maximization, epidemic forecasting, and information propagation research. | Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss. |
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