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| PageRank Bayesian× | PageRank Terarah× | |
|---|---|---|
| Bidang | Analisis Rangkaian | Analisis Rangkaian |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 1999 (PageRank); 2000s (Bayesian extension) | 1998 |
| Pengasas≠ | Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors | Brin, S. & Page, L. |
| Jenis≠ | Probabilistic centrality measure | Iterative authority-scoring algorithm |
| Sumber perintis≠ | Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report. link ↗ | Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th International Conference on World Wide Web (WWW7), 107–117. Elsevier. link ↗ |
| Alias | Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank | PageRank, PR, Google PageRank, directed link analysis |
| Berkaitan≠ | 6 | 5 |
| Ringkasan≠ | Bayesian PageRank extends the classic PageRank algorithm by embedding it within a Bayesian probabilistic framework. Instead of returning a single deterministic rank score for each node, it quantifies uncertainty over rank estimates — particularly valuable when the network is incomplete, noisy, or observed with error. It is used in web analysis, citation networks, and social network research where rank uncertainty matters. | Directed PageRank is a link-based authority scoring algorithm that assigns importance scores to nodes in a directed graph by iteratively redistributing rank through outgoing edges. Introduced by Brin and Page in 1998 as the backbone of Google Search, it measures not just how many in-links a node has but how authoritative the nodes pointing to it are. |
| ScholarGateSet data ↗ |
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