如何计算R中的邻接矩阵 [英] How to calculate adjacency matrices in R

问题描述

我有这个数据.我想在R中计算邻接矩阵.

I have this data. I want to calculate Adjacency matrices in R.

我该怎么做? V1，V2，V3是列.V1和V2是节点，W3是从V1到V2的权重.此数据中的方向很重要.计算邻接矩阵后，我想用R语言计算这些顶点之间的最短路径.

How can I do this? V1,V2,V3 are columns.V1 and V2 are NODES, and W3 are weight from V1 to V2. Direction in this data is important. After calculating the Adjacency matrices, I want to calculate shortest path between these vertices with R language.

我该怎么做?

      V1      V2     V3
[1] 164885   431072   3
[2] 164885   164885   24
[3] 431072   431072   5

推荐答案

这至少应该使您入门.我想到的最简单的方法是reshape，然后使用igraph构建图，如下所示:

This should at the least get your started. The simplest way I could think of to get the adjacency matrix is to reshape this and then build a graph using igraph as follows:

# load data
df <- read.table(header=T, stringsAsFactors=F, text="     V1      V2     V3
 164885   431072   3
 164885   164885   24
 431072   431072   5")
> df
#       V1     V2 V3
# 1 164885 431072  3
# 2 164885 164885 24
# 3 431072 431072  5

# using reshape2's dcast to reshape the matrix and set row.names accordingly
require(reshape2)
m <- as.matrix(dcast(df, V1 ~ V2, value.var = "V3", fill=0))[,2:3]
row.names(m) <- colnames(m)

> m
#        164885 431072
# 164885     24      3
# 431072      0      5

# load igraph and construct graph
require(igraph)
g <- graph.adjacency(m, mode="directed", weighted=TRUE, diag=TRUE)
> E(g)$weight # simple check
# [1] 24  3  5

# get adjacency
get.adjacency(g)

# 2 x 2 sparse Matrix of class "dgCMatrix"
#        164885 431072
# 164885      1      1
# 431072      .      1

# get shortest paths from a vertex to all other vertices
shortest.paths(g, mode="out") # check out mode = "all" and "in"
#        164885 431072
# 164885      0      3
# 431072    Inf      0

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