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| Centralitat de Proximitat Temporal× | PageRank Temporal× | |
|---|---|---|
| Camp | Anàlisi de xarxes | Anàlisi de xarxes |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2011 | 2016 |
| Autor original≠ | Pan, R. K. & Saramaki, J. | Rozenshtein, P. & Gionis, A. |
| Tipus≠ | Centrality measure (temporal) | Centrality / ranking algorithm for temporal networks |
| Font seminal≠ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ | 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 ↗ |
| Àlies | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank |
| Relacionats | 6 | 6 |
| Resum≠ | Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems. | Temporal PageRank extends the classic PageRank algorithm to time-evolving networks by incorporating the recency and ordering of interactions. Edges are weighted by a decay function so that recent contacts contribute more to a node's score than old ones. The result is a dynamic importance ranking that captures who is influential right now, rather than over the entire history of the network. |
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