Usporedite metode
Pregledajte odabrane metode jednu uz drugu; retci koji se razlikuju su istaknuti.
| Vremenska svojstvena centralnost× | Temporal PageRank× | |
|---|---|---|
| Područje | Analiza mreža | Analiza mreža |
| Obitelj | Machine learning | Machine learning |
| Godina nastanka≠ | 2011-2017 | 2016 |
| Tvorac≠ | Grindrod, P.; Higham, D. J.; Taylor, D. et al. | Rozenshtein, P. & Gionis, A. |
| Vrsta≠ | Centrality measure for temporal networks | Centrality / ranking algorithm for temporal networks |
| Temeljni izvor≠ | 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 ↗ |
| Drugi nazivi | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank |
| Srodne≠ | 5 | 6 |
| Sažetak≠ | 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. |
| ScholarGateSkup podataka ↗ |
|
|