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Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.4.1) |
| Suggests: | igraph (≥ 1.1.2), parallel (≥ 3.4.1), testthat (≥ 1.0.2) |
| Published: | 2017-09-06 |
| DOI: | 10.32614/CRAN.package.TNC |
| Author: | Moritz Hanke [aut, cre] |
| Maintainer: | Moritz Hanke <hanke at leibniz-bips.de> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | TNC results |
| Reference manual: | TNC.pdf |
| Package source: | TNC_0.1.0.tar.gz |
| Windows binaries: | r-devel: TNC_0.1.0.zip, r-release: TNC_0.1.0.zip, r-oldrel: TNC_0.1.0.zip |
| macOS binaries: | r-release (arm64): TNC_0.1.0.tgz, r-oldrel (arm64): TNC_0.1.0.tgz, r-release (x86_64): TNC_0.1.0.tgz, r-oldrel (x86_64): TNC_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.