Package 'node2vec'

Title: Algorithmic Framework for Representational Learning on Graphs
Description: 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]
Maintainer: Yang Tian <[email protected]>
License: GPL (>= 3)
Version: 0.1.0
Built: 2025-02-18 05:42:04 UTC
Source: https://github.com/cran/node2vec

Help Index

6 edges information between two genes of human

Description

A dataset containing the 6 interactions of genes

Usage

gene_edges

Format

A data frame with 6 rows and 2 variables:

gene1

human genes

gene2

human genes

Source

https://thebiogrid.org/

Algorithmic Framework for Representational Learning on Graphs

Description

Algorithmic Framework for Representational Learning on Graphs

Usage

node2vecR(
  data,
  p = NULL,
  q = NULL,
  directed = NULL,
  num_walks = NULL,
  walk_length = NULL,
  dim = NULL
)

Arguments

data

input data for edges consisting of at least two columns of data and if there are weights,it must be in the third column.
p

return parameter.Default to 1.
q

in-out parameter.Default to 1.
directed

the network is directed or undirected.Default to undirected.
num_walks

number of walks per node.Default to 10.
walk_length

number of nodes in each walk.Default to 80.
dim

embedding dimensions.Default to 128.

Value

embedding results for each node

Examples

#Parameters can be customized as needed
data(gene_edges)
use_data<-gene_edges
emb<-node2vecR(use_data,p=2,q=1,num_walks=5,walk_length=5,dim=10)