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| PageRank bayesià× | Centralitat del vector propi× | |
|---|---|---|
| Camp | Anàlisi de xarxes | Anàlisi de xarxes |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 1999 (PageRank); 2000s (Bayesian extension) | 1972 |
| Autor original≠ | Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors | Bonacich, P. |
| Tipus≠ | Probabilistic centrality measure | Centrality measure |
| Font seminal≠ | Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report. link ↗ | Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120. DOI ↗ |
| Àlies | Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank | eigenvector centrality, EC, Bonacich centrality, power centrality |
| Relacionats | 6 | 6 |
| Resum≠ | 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. | 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. |
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