igraph

问题 I have a large graph (several, actually) in igraph—on the order of 100,000 vertices—and each vertex has an attribute which is either true or false . For each vertex, I would like to count how many of the vertices directly connected to it have the attribute. My current solution is the following function, which takes as its argument a graph. attrcount <- function(g) { nb <- neighborhood(g,order=1) return(sapply(nb,function(x) {sum(V(g)$attr[x]})) } This returns a vector of counts which is off

问题 For each edge in a graph I would like to add an numeric attribute (weight) that is the product of an attribute (probability) of the incident vertices. I can do it by looping over the edges; that is: for (i in E(G)) { ind <- V(G)[inc(i)] p <- get.vertex.attribute(G, name = "prob", index=ind) E(G)[i]$weight <- prod(p) } However, this is qute slow for my graph (|V| ~= 20,000 and |E| ~= 200,000). Is there a faster way to do this operation? 回答1: Here is probably the fastest solution. The key is to

问题 I have a dataframe with X and Y coordinates of points like this: structure(list(X = c(666L, 779L, 176L, 272L, 232L, 74L, 928L, 667L, 1126L, 919L), Y = c(807, 518, 724, 221, 182, 807, 604, 384, 142, 728)), .Names = c("X", "Y"), row.names = c(NA, 10L), class = "data.frame") I just want to find out the shortest path connecting all these points, and also return its total distance. There are no other conditions: every point can be connected to any other, no specific point to start or end, no

问题 Now I have a data frame df1 : v1 v2 a 10 b 1 c 3 d 7 ....... And another data frame df2 : v1 v2 d a c a b c c d ... I'd like to plot a network based on df2 with igraph : plot(g, layout = layout_in_circle(g)) And the color of vertexes( a,b,c,d... ) should be in range of red to blue and the bigger the value in v2, the color of that vertex should be more closer to red. I have tried: require(igraph) g = graph.data.frame(df) plot(g, layout = layout_in_circle(g), vertex.color = color.scale(mention

问题 I used the fastgreedy algorithm in igraph for my community detection in a weighted, undirected graph. Afterwards I wanted to have a look at the modularity and I got different values for different methods and I am wondering why. I included a short example, which demonstrates my problem: library(igraph) d<-matrix(c(1, 0.2, 0.3, 0.9, 0.9, 0.2, 1, 0.6, 0.4, 0.5, 0.3, 0.6, 1, 0.1, 0.8, 0.9, 0.4, 0.1, 1, 0.5, 0.9, 0.5, 0.8, 0.5, 1), byrow=T, nrow=5) g<-graph.adjacency(d, weighted=T, mode="lower"

问题 I used the fastgreedy algorithm in igraph for my community detection in a weighted, undirected graph. Afterwards I wanted to have a look at the modularity and I got different values for different methods and I am wondering why. I included a short example, which demonstrates my problem: library(igraph) d<-matrix(c(1, 0.2, 0.3, 0.9, 0.9, 0.2, 1, 0.6, 0.4, 0.5, 0.3, 0.6, 1, 0.1, 0.8, 0.9, 0.4, 0.1, 1, 0.5, 0.9, 0.5, 0.8, 0.5, 1), byrow=T, nrow=5) g<-graph.adjacency(d, weighted=T, mode="lower"

问题 I am new to R and trying to figure out how to make a social network map of a system that I have data for. I have managed to figure out most of what I want to do from the FAQ and tutorials, but I am stuck on two things. How do I make the canvas larger / the graph more spaced out? Right now it's way too squashed. At the moment the edge thickness is set based on the weight. The weights represent different statuses (8 = Active, 3 = Requested, 2 = Hidden, 1 = Blocked) and I would like to style the

问题 I have a dataframe d : d<-structure(list(V1 = c(1L, 3L, 3L, 2L, 1L, 1L, 7L, 9L, 10L, 9L, 7L), V2 = c(2L, 4L, 5L, 5L, 4L, 6L, 8L, 3L, 1L, 8L, 5L)), .Names = c("V1", "V2"), class ="data.frame", row.names = c(NA, -11L)) g<-graph.data.frame(d,directed = F) I would assign to each vertex one or more colors depending on its affiliation variable given in a dataframe m m<-structure(list(vertex = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 9L, 9L, 10L, 1L, 1L, 6L, 6L), affilation = c(1L, 1L, 1L, 2L, 2L, 1L,

问题 I have a dataframe d : d<-structure(list(V1 = c(1L, 3L, 3L, 2L, 1L, 1L, 7L, 9L, 10L, 9L, 7L), V2 = c(2L, 4L, 5L, 5L, 4L, 6L, 8L, 3L, 1L, 8L, 5L)), .Names = c("V1", "V2"), class ="data.frame", row.names = c(NA, -11L)) g<-graph.data.frame(d,directed = F) I would assign to each vertex one or more colors depending on its affiliation variable given in a dataframe m m<-structure(list(vertex = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 9L, 9L, 10L, 1L, 1L, 6L, 6L), affilation = c(1L, 1L, 1L, 2L, 2L, 1L,

问题 I'm trying to calculate the clusters of a network using igraph in R, where all nodes are connected. The plot seems to work OK, but then I'm not able to return the correct groupings from my clusters. In this example, the plot shows 4 main clusters, but in the largest cluster, not all nodes are connected: I would like to be able to return the following list of clusters from this graph object: [[1]] [1] 8 9 [[2]] [1] 7 10 [[3]] [1] 4 6 11 [[4]] [1] 2 3 5 [[5]] [1] 1 3 5 12 Example code: library