Package: node2vec 0.1.0

node2vec: Algorithmic Framework for Representational Learning on Graphs

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 <arxiv:1607.00653>.

Authors:Yang Tian [aut, cre], Xu Li [aut], Jing Ren [aut]

node2vec_0.1.0.tar.gz
node2vec_0.1.0.zip(r-4.7)node2vec_0.1.0.zip(r-4.6)node2vec_0.1.0.zip(r-4.5)
node2vec_0.1.0.tgz(r-4.6-any)node2vec_0.1.0.tgz(r-4.5-any)
node2vec_0.1.0.tar.gz(r-4.7-any)node2vec_0.1.0.tar.gz(r-4.6-any)
node2vec_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
node2vec/json (API)

# Install 'node2vec' in R:

install.packages('node2vec', repos = c('https://tianyang-0523.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

gene_edges - 6 edges information between two genes of human

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 7 scripts 240 downloads 104 mentions 1 exports 33 dependencies

Last updated from:a3586cb1fd. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	163
source / vignettes	OK	185
linux-release-x86_64	OK	158
macos-release-arm64	OK	146
macos-oldrel-arm64	OK	129
windows-devel	OK	90
windows-release	OK	119
windows-oldrel	OK	87
wasm-release	OK	122