Machine learningNetwork science
动态PageRank
动态PageRank扩展了经典的PageRank算法，适用于具有时间戳的网络，并为节点分配随时间演变的重要性得分。通过对旧链接进行衰减并强调近期连接，它识别的是特定时刻而非整个网络历史中的节点影响力，因此非常适合网络存档、引用流、社交媒体级联以及任何链接时效性很重要的领域。
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来源
- 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: 10.1007/978-3-319-46227-1_42 ↗
- Berberich, K., Vazirgiannis, M., & Weikum, G. (2007). Time-aware authority ranking. Internet Mathematics, 3(4), 407–429. link ↗
如何引用本页
ScholarGate. (2026, June 3). Dynamic PageRank (Temporal Extension of the PageRank Algorithm). ScholarGate. https://scholargate.app/zh/network-analysis/dynamic-pagerank
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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