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| Centralitat de grau temporal× | PageRank Temporal× | |
|---|---|---|
| Camp | Anàlisi de xarxes | Anàlisi de xarxes |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2011–2012 | 2016 |
| Autor original≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Rozenshtein, P. & Gionis, A. |
| Tipus≠ | Centrality measure (temporal extension) | Centrality / ranking algorithm for temporal networks |
| Font seminal≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. 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 degree centrality, dynamic degree centrality, temporal node degree, TDC | TPR, time-aware PageRank, streaming PageRank, dynamic PageRank |
| Relacionats | 6 | 6 |
| Resum≠ | Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window. | 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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