方法对比
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| 定向网络扩散分析× | 社会网络分析× | |
|---|---|---|
| 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 | 1934 (sociometry); 1994 (modern formalization) |
| 提出者≠ | Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading) | Moreno, J.L.; formalized by Wasserman & Faust |
| 类型≠ | Network spreading and cascade analysis | Structural/relational 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 ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| 别名 | directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation | SNA, network analysis, sociometric analysis, relational analysis |
| 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. | 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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