在 R 中操作网络数据

发布于 2024-07-29 01:54:31 字数 250 浏览 4 评论 0原文

我有一个数据框，详细说明了 N 个节点之间的边权重。有处理此类数据的包吗？

例如，我想将以下信息绘制为网络：

  p1 p2 counts
1  a  b    100
2  a  c    200
3  a  d    100
4  b  c     80
5  b  d     90
6  b  e    100
7  c  d    100
8  c  e     40
9  d  e     60

原文

I have a data frame detailing edge weights among N nodes. Is there a package for working with this sort of data?

For example, I would like to plot the following information as a network:

  p1 p2 counts
1  a  b    100
2  a  c    200
3  a  d    100
4  b  c     80
5  b  d     90
6  b  e    100
7  c  d    100
8  c  e     40
9  d  e     60

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陈独秀 2024-08-05 01:54:31

一种选择是 network 包，它是 statnet R 软件包系列，用于统计社交网络分析。它以稀疏的方式处理网络数据，这对于较大的数据集很有用。

下面，我执行以下操作：

将边缘列表（前两列）加载到网络对象中
，将计数分配为称为权重的边缘属性。
使用 gplot 绘制网络图。（有关更改边缘厚度的信息，请参阅帮助页面。）
绘制社会矩阵（仅表示邻接矩阵的一组 5x5 块，其中 (i,j) 单元格由相对计数遮蔽）

A = read.table(file="so.txt",header=T)
A
      p1 p2 counts
    1  a  b    100
    2  a  c    200
    3  a  d    100
    4  b  c     80
    5  b  d     90
    6  b  e    100
    7  c  d    100
    8  c  e     40
    9  d  e     60

library(network)
net = network(A[,1:2])
# Get summary information about your network
net
     Network attributes:
      vertices = 5 
      directed = TRUE 
      hyper = FALSE 
      loops = FALSE 
      multiple = FALSE 
      bipartite = FALSE 
      total edges= 9 
        missing edges= 0 
        non-missing edges= 9 
        Vertex attribute names: 
        vertex.names 
     adjacency matrix:
      a b c d e
    a 0 1 1 1 0
    b 0 0 1 1 1
    c 0 0 0 1 1
    d 0 0 0 0 1
    e 0 0 0 0 0

set.edge.attribute(net,"weight",A[,3])
gplot(net)

## Another cool feature
s = as.sociomatrix(net,attrname="weight")
plot.sociomatrix(s)

One option is the network package, part of the statnet family of R packages for statistical social network analysis. It handles network data in a sparse way, which is nice for larger data sets.

Below, I do the following:

load the edgelist (the first two columns) into a network object
assign the counts to be an edge attribute called weight.
plot the network with gplot. (See the help page for changing the thickness of the edges.)
plot a sociomatrix (just a 5x5 set of blocks representing the adjacency matrix, where the (i,j) cell is shaded by the relative count)

A = read.table(file="so.txt",header=T)
A
      p1 p2 counts
    1  a  b    100
    2  a  c    200
    3  a  d    100
    4  b  c     80
    5  b  d     90
    6  b  e    100
    7  c  d    100
    8  c  e     40
    9  d  e     60

library(network)
net = network(A[,1:2])
# Get summary information about your network
net
     Network attributes:
      vertices = 5 
      directed = TRUE 
      hyper = FALSE 
      loops = FALSE 
      multiple = FALSE 
      bipartite = FALSE 
      total edges= 9 
        missing edges= 0 
        non-missing edges= 9 
        Vertex attribute names: 
        vertex.names 
     adjacency matrix:
      a b c d e
    a 0 1 1 1 0
    b 0 0 1 1 1
    c 0 0 0 1 1
    d 0 0 0 0 1
    e 0 0 0 0 0

set.edge.attribute(net,"weight",A[,3])
gplot(net)

## Another cool feature
s = as.sociomatrix(net,attrname="weight")
plot.sociomatrix(s)

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草莓酥 2024-08-05 01:54:31

以下是如何在 igraph 中绘制数据的网络图：

d <- data.frame(p1=c('a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd'),
                p2=c('b', 'c', 'd', 'c', 'd', 'e', 'd', 'e', 'e'),
                counts=c(100, 200, 100,80, 90,100, 100,40,60))

library(igraph)
g <- graph.data.frame(d, directed=TRUE)
print(g, e=TRUE, v=TRUE)
tkplot(g, vertex.label=V(g)$name)

Here's how to make a network plot of the data in igraph:

d <- data.frame(p1=c('a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd'),
                p2=c('b', 'c', 'd', 'c', 'd', 'e', 'd', 'e', 'e'),
                counts=c(100, 200, 100,80, 90,100, 100,40,60))

library(igraph)
g <- graph.data.frame(d, directed=TRUE)
print(g, e=TRUE, v=TRUE)
tkplot(g, vertex.label=V(g)$name)

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