My problem is very simple: I need to create an adjacency list/matrix from a list of edges.
I have an edge list stored in a csv document with column1 = node1 and column2 = node2 and I would like to convert this to a weighted adjacency list or a weighted adjacency matrix.
To be more precise, here's how the data looks like -where the numbers are simply node ids:
node1,node2
551,548
510,512
548,553
505,504
510,512
552,543
512,510
512,510
551,548
548,543
543,547
543,548
548,543
548,542
Any tips on how to achieve the conversion from this to a weighted adjacency list/matrix? This is how I resolved to do it previously, without success (courtesy of Dai Shizuka):
dat=read.csv(file.choose(),header=TRUE) # choose an edgelist in .csv file format
el=as.matrix(dat) # coerces the data into a two-column matrix format that igraph likes
el[,1]=as.character(el[,1])
el[,2]=as.character(el[,2])
g=graph.edgelist(el,directed=FALSE) # turns the edgelist into a 'graph object'
Thank you!
This response uses base R only. The result is a standard matrix used to represent the adjacency matrix.
el <- cbind(a=1:5, b=5:1) #edgelist (a=origin, b=destination)
mat <- matrix(0, 5, 5)
mat[el] <- 1
mat
# [,1] [,2] [,3] [,4] [,5]
#[1,] 0 0 0 0 1
#[2,] 0 0 0 1 0
#[3,] 0 0 1 0 0
#[4,] 0 1 0 0 0
#[5,] 1 0 0 0 0
Here mat is your adjacency matrix defined from edgelist el, which is a simple cbind of the vectors 1:5 and 5:1.
If your edgelist includes weights, then you need a slightly different solution.
el <- cbind(a=1:5, b=5:1, c=c(3,1,2,1,1)) # edgelist (a=origin, b=destination, c=weight)
mat<-matrix(0, 5, 5)
for(i in 1:NROW(el)) mat[ el[i,1], el[i,2] ] <- el[i,3] # SEE UPDATE
mat
# [,1] [,2] [,3] [,4] [,5]
#[1,] 0 0 0 0 3
#[2,] 0 0 0 1 0
#[3,] 0 0 2 0 0
#[4,] 0 1 0 0 0
#[5,] 1 0 0 0 0
UPDATE
Some time later I realized that the for loop (3rd line) in the previous weighted edgelist example is unnecessary. You can replace it with the following vectorized operation:
mat[el[,1:2]] <- el[,3]
The post on my website you mention in the question (https://sites.google.com/site/daishizuka/toolkits/sna/sna_data) uses the igraph package, so make sure that is loaded.
Moreover, I recently realized that igraph provides a much easier way to create a weighted adjacency matrix from edgelists, using graph.data.frame(). I've updated this on my site, but here is a simple example:
library(igraph)
el=matrix(c('a','b','c','d','a','d','a','b','c','d'),ncol=2,byrow=TRUE) #a sample edgelist
g=graph.data.frame(el)
get.adjacency(g,sparse=FALSE)
That should do it. The sparse=FALSE argument tells it to show the 0s in the adjacency matrix. If you really don't want to use igraph, I think this is a clunky way to do it:
el=matrix(c('a','b','c','d','a','d','a','b','c','d'),ncol=2,byrow=TRUE) #a sample edgelist
lab=names(table(el)) #extract the existing node IDs
mat=matrix(0,nrow=length(lab),ncol=length(lab),dimnames=list(lab,lab)) #create a matrix of 0s with the node IDs as rows and columns
for (i in 1:nrow(el)) mat[el[i,1],el[i,2]]=mat[el[i,1],el[i,2]]+1 #for each row in the edgelist, find the appropriate cell in the empty matrix and add 1.
Start with your data frame edges and use igraph to obtain adjacency matrix:
head(edges)
node1 node2
1 551 548
2 510 512
3 548 553
4 505 504
5 510 512
6 552 543
library(igraph)
as.matrix(get.adjacency(graph.data.frame(edges)))
551 510 548 505 552 512 543 553 504 547 542
551 0 0 2 0 0 0 0 0 0 0 0
510 0 0 0 0 0 2 0 0 0 0 0
548 0 0 0 0 0 0 2 1 0 0 1
505 0 0 0 0 0 0 0 0 1 0 0
552 0 0 0 0 0 0 1 0 0 0 0
512 0 2 0 0 0 0 0 0 0 0 0
543 0 0 1 0 0 0 0 0 0 1 0
553 0 0 0 0 0 0 0 0 0 0 0
504 0 0 0 0 0 0 0 0 0 0 0
547 0 0 0 0 0 0 0 0 0 0 0
542 0 0 0 0 0 0 0 0 0 0 0
Another possibility with the qdapTools package:
library(qdapTools)
el[rep(seq_len(nrow(el)), el[,'c']), c('a', 'b')] %>%
{split(.[,'b'], .[,'a'])} %>%
mtabulate()
## 1 2 3 4 5
## 1 0 0 0 0 3
## 2 0 0 0 1 0
## 3 0 0 2 0 0
## 4 0 1 0 0 0
## 5 1 0 0 0 0
来源：https://stackoverflow.com/questions/16584948/how-to-create-weighted-adjacency-list-matrix-from-edge-list