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| PageRank Dinamik× | Pusat Teras Eigenvector× | |
|---|---|---|
| Bidang | Analisis Rangkaian | Analisis Rangkaian |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2007–2016 | 1972 |
| Pengasas≠ | Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank | Bonacich, P. |
| Jenis≠ | Centrality / ranking algorithm | Centrality measure |
| Sumber perintis≠ | 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 ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Alias | Temporal PageRank, time-aware PageRank, evolving PageRank, DPR | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Berkaitan | 6 | 6 |
| Ringkasan≠ | 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. | Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network. |
| ScholarGateSet data ↗ |
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