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| Sentraliti Eigenvektor Temporal× | PageRank Temporal× | |
|---|---|---|
| Bidang | Analisis Rangkaian | Analisis Rangkaian |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2011-2017 | 2016 |
| Pengasas≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Rozenshtein, P. & Gionis, A. |
| Jenis≠ | Centrality measure for temporal networks | Centrality / ranking algorithm for temporal networks |
| Sumber perintis≠ | Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. 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 ↗ |
| Alias | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank |
| Berkaitan≠ | 5 | 6 |
| Ringkasan≠ | Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network. | 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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