Given any graph, the 'node2vec' algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <doi:10.48550/arXiv.1607.00653>.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | data.table, igraph, word2vec, rlist, dplyr, vctrs, vegan |
| Published: | 2021-01-14 |
| Author: | Yang Tian [aut, cre], Xu Li [aut], Jing Ren [aut] |
| Maintainer: | Yang Tian <tianyang1211 at 126.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | node2vec results |
| Reference manual: | node2vec.pdf |
| Package source: | node2vec_0.1.0.tar.gz |
| Windows binaries: | r-prerel: node2vec_0.1.0.zip, r-release: node2vec_0.1.0.zip, r-oldrel: node2vec_0.1.0.zip |
| macOS binaries: | r-prerel (arm64): node2vec_0.1.0.tgz, r-release (arm64): node2vec_0.1.0.tgz, r-oldrel (arm64): node2vec_0.1.0.tgz, r-prerel (x86_64): node2vec_0.1.0.tgz, r-release (x86_64): node2vec_0.1.0.tgz |
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