The following example gives shortest path 1-2-6-7-3-4, where only the weight of edges is considered; and the weight of turn at vertices is not counted for. Can someone suggest a procedure to include the weight at each vertex that is no-turn, right-turn, or left-turn? We can assume the weight for (NT, RT, LT)=(0,0.5,1). When edge weight is combined with turn effect, the shortest path would become 1-2-3-4. Below is the example in question. Thank you.
#
library(igraph)
n <- c(1,2,3,4,5,6,7,8)
x <- c(1,4,7,10,1,4,7,10)
y <- c(1,1,1,1,4,4,4,4)
node <- data.frame(n,x,y)
fm <- c(1,2,3,5,6,7,1,2,3,4)
to<-c(2,3,4,6,7,8,5,6,7,8)
weight<- c(1,4,1,1,1,2,5,1,1,1)
link <- data.frame(fm,to,weight)
g <- graph.data.frame(link,directed=FALSE,vertices=node)
sv <- get.shortest.paths(g,1,4,weights=NULL,output="vpath")
sv
E(g)$color <- "pink"
E(g, path=sv[[1]])$width <- 8
plot(g,edge.color="red")
plot(g,edge.label=weight,edge.label.color="blue",edge.label.cex=2)
As a preprocessing step: for each vertex v with a incoming edges and b outgoing edges, split it into a vertexes connected to those incoming edges and b vertexes connected to those outgoing edges. Then create edges representing turning costs in between.
In principal, Jeffery is describing what we want, but the problem size is such that we need a programmatic solution. Maybe 200,000 vertices with 3 to 6 edges. If there is a way to explode, for instance, the standard intersection of 4 edges in and 4 edges out to the 16 right through left movements and automatically assigning left through and right penalties.
most important is the ability to have lesser penalties for turning at T intersections (ease of wayfinding) than turning at traditional intersections/vertices
Is this tractable for a huge network?
Related
how can I create a complete list of dyads from a vertex list?
I have a list (1, 2, 3...) and I need to generate a list containing all possible dyads from that list (1-1, 1-2, 1-3, 2-1, 2-2,...).
I've tried with get.edgelist, but it doesn't work, because the graph is not fully connected (all nodes are connected among them).
Thanks
Using igraph, you can grab all edges of a graph using E(g). If you'd want all possible edges, you can apply it on a complete graph (a graph that is fully connected). If the vertices in your graph are indeed in sequence from 1 to n, you can use make_full_graph() to make a Kn - that is to say a fully connected graph. In this example, the graph has 14 vertices.
g <- make_full_graph(14, directed=F)
el <- as_edgelist(g)
edges <- E(g)
edges_list <- split(el, rep(1:nrow(el), each = ncol(el)))
edges_vert <- unlist(list(t(el)))
edges will be the igraph-object, but I think what you're after is a list in R, like edges_list.
As you see, length(edges_list) is 91 since it is an undirected graph, and the number of edges in complete graphs is a function of the number of vertices.
A complete graph with n vertices is commonly written Kn and has these many edges:
Note that in igraph dyads are called edges and nodes are called vertices.
I'm fairly new to IGraph in R.
I'm doing community detection using IGraph and have already built my communities /clusters using the walktrap technique.
Next, within each cluster, I want to count the number of vertices between each two certain vertices. The reason I want to do this is, for each vertex XX, I want to list vertices that are connected to XX via say max 3 vertices, meaning no further than 3 vertices away from XX.
Can anyone help how this can be done in R please?
making a random graph (for demonstration):
g <- erdos.renyi.game(100, 1/25)
plot(g,vertex.size=3)
get walktrap communities and save as vertex attribute:
V(g)$community<-walktrap.community(g, modularity = TRUE, membership = TRUE)$membership
V(g)$community
now make a subgraph containing only edges and vertices of one community, e.g. community 2:
sub<-induced.subgraph(g,v=V(g)$community==2)
plot(sub)
make a matrix containing all shortest paths:
shortestPs<-shortest.paths(sub)
now count the number of shortest paths smaller or equal to 3.
I also exclude shortest paths from each node to itself (shortestPaths!=0).
also divide by two because every node pair appears twice in the matrix for undirected graphs.
Number_of_shortest_paths_smaller_3 <- length(which(shortestPs<=3 & shortestPs!=0))/2
Number_of_shortest_paths_smaller_3
Hope that's close to what you need, good luck!
I have 4 undirected graph with 1000 vertices and 176672, 150994, 193477, 236060 edges. I am trying to see interaction between a specific set of nodes (16 in number) for each graph. This visualization in tkplot is not feasible as 1000 vertices is already way too much for it. I was thinking of if there is some way to extract the interaction of these 16 nodes from the parent graph and view separately, which will be then more easy to handle and work with in tkplot. I don't want the loss of information as in what is the node(s) in he path of interaction if it comes from other than 16 pre-specified nodes. Is there a way to achieve it?
In such a dense graph, if you only take the shortest paths connecting each pair of these 16 vertices, you will still get a graph too large for tkplot, or even to see any meaningful on a cairo pdf plot.
However, if you aim to do it, this is one possible way:
require(igraph)
g <- erdos.renyi.game(n = 1000, p = 0.1)
set <- sample(1:vcount(g), 16)
in.shortest.paths <- NULL
for(v in set){
in.shortest.paths <- c(in.shortest.paths,
unlist(get.all.shortest.paths(g, from = v, to = set)$res))
}
subgraph <- induced.subgraph(g, unique(in.shortest.paths))
In this example, subgraph will include approx. half of all the vertices.
After this, I think you should consider to find some other way than visualization to investigate the relationships between your vertices of interest. It can be some topological metric, but it really depends on the aims of your analysis.
I have a network with two types of edges, one with edge$weight=1 and the other edge$weight=2 (to define different relationships e.g. marriage vs. kinship). I want to choose the neighbors of nodes that are linked by edge$weight=2. Then, after getting the vertex names of the neighbors, I will calculate their degree only for edge$weight=1.
It seems simple, but the following code does not give the correct neighbor names:
V(net)[E(net)[E(net)$weight==2]]
Is there another way to get vertex names with respect to the edge type?
I think you are looking for the adj function which will return the vertices adjacent to a given edge set. In your case you can choose all the relevant vertices by using V(net)[ adj(E(net)$weight==2) ]
For example:
# make a sample graph with edge weights 1 or 2
net <- graph.full(20)
E(net)$weight <- sample(1:2,ecount(net),replace=T)
# now select only those vertices connected by an edge with weight == 2
V(net)[ adj(E(net)$weight==2) ]
I have a data table which consists of names of users who post in the same thread in a forum, it looks like that:
X1 X2
1. g79 kian
2. g79 greyracer
3. g79 oldskoo1 ...
I need to calculate degree, closeness and betweenness. I'm using the following code:
library(igraph)
setwd("/Volumes/NATASHKA/api/R files")
load("edgelist_one_mode.rda")
load("map.rda")
load ("result.rda")
el <- as.matrix(whatwewant)
el[,1] <- as.character(el[,1])
el[,2] <- as.character(el[,2])
g <- graph.data.frame(el, directed=FALSE)
plot(g, edge.arrow.size=.5)
indegreeG <- degree(g, mode="in")
outdegreeG <- degree(g, mode="out")
totaldegreeG <- degree(g)
inclosenessG <- closeness(g, mode='in')
outclosenessG <- closeness(g, mode='out')
totalclosenessG <- closeness(g)
betweennessG <- betweenness(g)
forumG <- data.frame(V(g)$name, indegreeG, outdegreeG, totaldegreeG, inclosenessG, outclosenessG, totalclosenessG, betweennessG)
write.table(forumG,file="forumG.csv",sep=";")
The question is why do I get the same values for in-degree, out-degree and total-degree, the same for closeness? Besides, at the beginning I have 41213 users, but after analysis (when I calculate degree, etc..) I only have 37874. How could I lose so many observations? Please tell me if I have a mistake in the code.
Thanks
The reason you get the same value for in-degree, out-degree and total degree is because you are creating an undirected network with the graph.data.frame(el, directed=FALSE).
In an undirected network, the number of links from a node and to a node are the same and they are both equal to the global degree.
If you want a directed network, you will need to do graph.data.frame(el, directed=TRUE).
It will create a directed network in which the id in the first column of your dataframe is the id of the node sending the tie and the id in the second column indicates the node receiving that tie.
As for loosing nodes, my guess would be that you have some individuals who never interact with anyone and therefore are lost when you transform your two-mode network into one-mode (I assume you do this but don't show us how you do it because of your line:load("edgelist_one_mode.rda"))
Short of a reproducible example, I think that is all I can deduce from your code.