Machine learningNetwork science
时间PageRank
时间PageRank通过纳入交互的近期性和顺序，将经典的PageRank算法扩展到时变网络。边根据衰减函数加权，使得近期联系对节点得分的贡献大于旧联系。其结果是一个动态重要性排序，能够捕捉当前谁具有影响力，而不是在整个网络历史中。
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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), Part II, LNCS 9852, pp. 674–689. Springer. DOI: 10.1007/978-3-319-46227-1_42 ↗
- Lerman, K. & Ghosh, R. (2010). Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 90–97. AAAI Press. link ↗
如何引用本页
ScholarGate. (2026, June 3). Temporal PageRank (Time-Aware Node Importance Ranking in Temporal Networks). ScholarGate. https://scholargate.app/zh/network-analysis/temporal-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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