Salīdzināt metodes
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| Temporālais PageRank× | Temporālā sociālo tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2016 | 2000s–2010s |
| Autors≠ | Rozenshtein, P. & Gionis, A. | Moody, J.; Holme, P.; Saramäki, J. |
| Tips≠ | Centrality / ranking algorithm for temporal networks | Longitudinal network analysis |
| Pirmavots≠ | 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 ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Citi nosaukumi | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | 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. | Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time. |
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