I use igraph package in R and create a simple graph named g. In the first row of the output using str(g), I figured the first letter can be U or D for undirected and directed graphs. The third letter can be W for weighted graph. The fourth letter can be B for bipartite graph.
What does the second letter mean? Hopefully my understanding is right for the other three letters. I tried the igraph help file but can't find an answer. Thanks.
> g <- graph.formula(1-2, 1-3, 1-4, 2-4)
> str(g)
IGRAPH UN-- 4 4 --
+ attr: name (v/c)
+ edges (vertex names):
[1] 1--2 1--3 1--4 2--4
N is for named graph. See ?is.named.
And yes for the other three letters.
Documentation: see details in ?str.igraph or ?print.igraph
Related
In Python you can simply use graph.select() (atleast when reading the documentation: https://igraph.org/python/doc/api/igraph.VertexSeq.html) to select a vertex based on a value. I have a huge graph connecting movies that share an actor. However I would simply like to select one vertex from that graph and then plot it with its direct neighbors. I'm running into issues when I want to select just that single vertex.
I had hoped something like worked
graph.movies.select('Snatch (2000)')
But no luck.
Another approach I took was grabbing the single Snatch vertex by filtering all others out and then adding the edges.
snatch.graph <- induced.subgraph(g.movies, vids=V(g.movies)$name == 'Snatch (2000)')
snatch.edges <- edges(g.movies, "Snatch (2000)")
add_edges(snatch.graph, snatch.edges$edges)
However this returns an empty graph with only the snatch vertex.
My goal is to grab the Snatch vertex and plot this vertex, its DIRECT neighbors and the edges amomng them. Any suggestions? Thanks alot :D Been stuck o nthis for a quite a while -.-
You can use ?ego to grab the neighbours or ?make_ego_graph to form the graph. (Depending on whether your graph is directed or not, you may need to use the mode argument).
An example:
library(igraph)
# create some data
set.seed(71085002)
n = 10
g = random.graph.game(n, 0.25)
V(g)$name = seq_len(n)
# grab neighbours of node "1"
# set mindist = 0 to include node itself
# set order for the stepsize of the neigbourhood;
nb_g = make_ego_graph(g, order=1, node="1", mindist=0)[[1]]
# plot
# you could use the `layout` argument to
# keep nodes in the same position
op = par(mfrow=c(1,2), oma=rep(0,4))
plot(g, vertex.size=0, vertex.label.cex=3, main="Full graph")
plot(nb_g, vertex.size=0, vertex.label.cex=3, main="Sub graph")
par(op) # reset
If you just need a list of the neighbouring nodes:
ego(g, order=1, node="1", mindist=0)
# [[1]]
# + 4/10 vertices, named, from 00cfa70:
# [1] 1 4 6 9
I think the method using ego (by #user20650) is comprehensive and efficient.
Here is another option if you would like to find the sub-graph built on direct neighbors, which applies distances + induced_subgraph
> induced_subgraph(g, which(distances(g, "1") <= 1))
IGRAPH 99f872b UN-- 4 3 -- Erdos renyi (gnp) graph
+ attr: name (g/c), type (g/c), loops (g/l), p (g/n), name (v/n)
+ edges from 99f872b (vertex names):
[1] 1--4 1--6 1--9
I am doing some basic network analysis using networks from the R package "networkdata". To this end, I use the package "igraph" as well as "sna". However, I realised that the results of descriptive network statistics vary depending on the package I use. Most variation is not too grave but the average degree of my undirected graph halved as soon as I switched from "sna" to "igraph".
library(networkdata)
n_1 <- covert_28
library(igraph)
library(sna)
n_1_adjmat <- as_adjacency_matrix(n_1)
n_1_adjmat2 <- as.matrix(n_1_adjmat)
mean(sna::degree(n_1_adjmat2, cmode = "freeman")) # [1] 23.33333
mean(igraph::degree(n_1, mode = "all")) # [1] 11.66667
This doesn't happen in case of my directed graph. Here, I get the same results regardless of using "sna" or "igraph".
Is there any explanation for this phenomenon? And if so, is there anything I can do in order to prevent this from happening?
Thank you in advance!
This is explained in the documentation for sna::degree.
indegree of a vertex, v, corresponds to the cardinality
of the vertex set N^+(v) = {i in V(G) : (i,v) in E(G)};
outdegree corresponds to the cardinality of the vertex
set N^-(v) = {i in V(G) : (v,i) in E(G)}; and total
(or “Freeman”) degree corresponds to |N^+(v)| + |N^-(v)|.
(Note that, for simple graphs,
indegree=outdegree=total degree/2.)
A simpler example than yours makes it clear.
library(igraph)
library(sna)
g = make_ring(3)
plot(g)
AM = as.matrix(as_adjacency_matrix(g))
sna::degree(AM)
[1] 4 4 4
igraph::degree(g)
[1] 2 2 2
Vertex 1 has links to both vertices 2 and 3. These count in the
in-degree and also count in the out-degree, so
Freeman = in + out = 2 + 2 = 4
The "Note" in the documentation states this.
I want to find the maximum number of nodes which can be reached from each node in a graph using igraph in R.
For example, I have the following graph:
IGRAPH fb9255f DN-- 4 3 --
+ attr: name (v/c), X (e/l)
+ edges from fb9255f (vertex names):
1 1->2 2->3 2->4
Resulting graph
For node 1, for example, I would like to obtain the list of all the possible
nodes reachable (not only using one hop) from it.
In this case, for node 1 it will be: [2,3,4]
I have read the igraph documentation, but I do not see any function that can help.
Any help will be appreciated.
Thanks
Carlos
You can compute this using the subcomponent function.
Since you do not provide any data, I will illustrate with an arbitrary example.
## Example graph
library(igraph)
set.seed(123)
g = erdos.renyi.game(15, 0.15, directed = TRUE)
plot(g)
subcomponent gives you all of nodes that are reachable. Here, I am assuming that you are using directed graphs and you mean reachable by going forward along the directed edges. You can change this by altering the mode argument to subcomponent.
sort(subcomponent(g, 2, mode="out"))
+ 7/15 vertices:
[1] 2 5 10 12 13 14 15
If you just want the number of nodes that can be reached, just take the length
length(subcomponent(g, 2, mode="out"))
[1] 7
I identify the neighbors of a selected node but haven’t been able to plot the result. Take the following example, copied from another question:
edgelist <- read.table(text = "
A B
B C
C D
D E
C F
F G")
library(igraph)
graph <- graph.data.frame(edgelist)
str(graph)
#IGRAPH DN-- 7 6 --
# + attr: name (v/c)
# + edges (vertex names):
# [1] A->B B->C C->D D->E C->F F->G
I identify the neighbors of "D" with:
neighborsD <- neighbors(graph, "D")
But when I instruct R to plot "neighborsD"...
plot(neighborsD)
... I get a chart instead of a sociogram, and when I try to tkplot it I get the error "not a graph object". So two questions:
1) How do I plot the network around, say, "D"?
2) How do I plot “D”, its neighbors, and the neighbors of the neighbors (two steps from "D"?
Use the ego() function to find nodes that are a certain distance away from a a node. And then use induced_subgraph to subset your main graph. For example, the does that are 1 step away are
plot(induced_subgraph(graph, ego(graph, 1, "D")[[1]]))
and those that are two steps away are
plot(induced_subgraph(graph, ego(graph, 2, "D")[[1]]))
I have a list of networks of family names. Each network is composed of two different subgraphs (for instance, net_277_278 is composed of subgraphs sg_277 and sg_278). (I induced the subgraphs using a complicated mix of the gsub and induced_subgraph functions).
All the edges in the same subgraph have the form Johnson_278--Smith_278, indicating a link between two surnames belonging to the same subgraph. Links between the two subgraphs are given by the same surname (but have a different subscript). A link between the two subgraphs looks like this: Johnson_277--Johnson_278.
For each subgraph, I want to to compute some network measures (e.g., centrality), but only for the nodes that are directly connected to the other subgraph. (Note that I do not want to induce new subgraphs, since this would alter the measures). For instance, for sg_277 I would like to compute some measures only for Johnson_277, if that's the only node with a link to the sg_278.
As a small example, this is one of the networks:
net[10] #277_278
$11_277_278
IGRAPH UN-- 9 6 --
+ attr: name (v/c)
+ edges (vertex names):
[1] SANCHEZ_110277--SANCHEZ_110278 SANCHEZ_110277--PANTOJA_110277 SANCHEZ_110278--GALVAN_110278
[4] PEREZ_110278 --VEGA_110278 PEREZ_110278 --OLVERA_110278 PATIÑO_110278 --SERRANO_110278
Graphically, it looks like this
And this is one of the subgraphs:
sgraph1[[10]] #277
IGRAPH UN-- 2 1 --
+ attr: name (v/c)
+ edge (vertex names):
[1] SANCHEZ_110277--PANTOJA_110277
In this case, I would like to run the function for eigenvector centrality (evcent(sgraph1[[10]])$vector), only for the nodes linking the two subgraphs (in this case, just SANCHEZ_110277, which, as can be seen in the full network, has a link to SANCHEZ_110278.
Is there a way to do this? I have been trying stuff using regular expressions, neighbors and ego() functions but none of these seem to help.