方法对比
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| 动态PageRank× | 时态社群检测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2007–2016 | 2010 |
| 提出者≠ | Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank | Mucha, P. J. et al. |
| 类型≠ | Centrality / ranking algorithm | Network clustering algorithm |
| 开创性文献≠ | Rozenshtein, P., & Gionis, A. (2016). Temporal PageRank. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Lecture Notes in Computer Science, 9853, 674–689. Springer. 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 | Temporal PageRank, time-aware PageRank, evolving PageRank, DPR | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| 相关 | 6 | 6 |
| 摘要≠ | Dynamic PageRank extends the classic PageRank algorithm to networks whose edges carry timestamps, assigning importance scores that evolve over time. By discounting older links and emphasising recent connections, it identifies nodes that are influential at specific moments rather than across the entire network history, making it well-suited for web archives, citation streams, social media cascades, and any domain where link recency matters. | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. |
| ScholarGate数据集 ↗ |
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