方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 动态PageRank× | 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≠ | 2007–2016 | 2010 (key formalization); earlier work 2002–2009 |
| 提出者≠ | Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) |
| 类型≠ | Centrality / ranking algorithm | Graph clustering / community discovery |
| 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 | DCD, temporal community detection, evolving community detection, dynamic graph clustering |
| 相关≠ | 6 | 5 |
| 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. | 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. |
| ScholarGate数据集 ↗ |
|
|