igraph

Consider a dataframe df where the first two columns are node pairs and successive columns V1 , V2 , ..., Vn represent flows between the nodes (potentially 0, implying no edge for that column's network). I would like to conduct analysis on degree, community detection, and other network measures using the flows as weights. Then to analyze the graph with respect to the weights in V1 I do: # create graph and explore unweighted degrees with respect to V1 g <- graph.data.frame( df[df$V1!=0,] ) qplot(degree(g)) x <- 0:max(degree(g)) qplot(x,degree.distribution(g)) # set weights and explore weighted

Suppose I want to make a plot with the following data: pairs <- c(1, 2, 2, 3, 2, 4, 2, 5, 2, 6, 2, 7, 2, 8, 2, 9, 2, 10, 2, 11, 4, 14, 4, 15, 6, 13, 6, 19, 6, 28, 6, 36, 7, 16, 7, 23, 7, 26, 7, 33, 7, 39, 7, 43, 8, 35, 8, 40, 9, 21, 9, 22, 9, 25, 9, 27, 9, 33, 9, 38, 10, 12, 10, 18, 10, 20, 10, 32, 10, 34, 10, 37, 10, 44, 10, 45, 10, 46, 11, 17, 11, 24, 11, 29, 11, 30, 11, 31, 11, 33, 11, 41, 11, 42, 11, 47, 14, 50, 14, 52, 14, 54, 14, 55, 14, 56, 14, 57, 14, 58, 14, 59, 14, 60, 14, 61, 15, 48, 15, 49, 15, 51, 15, 53, 15, 62, 15, 63) g <- graph( pairs ) plot( g,layout = layout.reingold.tilford

I want to change the edge width of my graph to correspond to the edge.betweenness score. net <- read.csv("D:/SNA/R/Net.csv") att <- read.csv("D:/SNA/R/Att.csv") g <- graph.data.frame(net, vertices=att, directed=TRUE) pdf("Network.pdf", pointsize=8) plot(g, vertex.label=NA, vertex.size=3, edge.width=edge.betweenness(g)) dev.off() I have also tried creating the edge betweenness score as an edge weight and assigning it to edge.width argument in the plot function as follows; plot(g, vertex.label=NA, vertex.size=3, edge.width=E(g)$width Your example should work. Alternatively, you can write E(g)

I was wondering if you guys can help me building an adjacency matrix. I have data in CVS format like this: Paper_ID Author 2 Foster-McGregor, N. 3 Van Houte, M. 4 van de Meerendonk, A. 5 Farla, K. 6 van Houte, M. 6 Siegel, M. 8 Farla, K. 11 Farla, K. 11 Verspagen, B. As you can see the column "Paper_ID" has a repeated value of 11, meaning that "Farla, K." and "Verspagen, B." are coauthors of a publication. I need to build a square weighted matrix using the names of the authors, counting the times that they are collaborating together. Does the following do what you are looking for? # simulate

I have created a random (Erdos-Renyi) network that has 100 nodes. I have set an attribute value for all 100 nodes as 0. I find the node with the maximum degree (the most neighbors), and change its attribute value from 0 to 1. Then, using the node as the root node, I do a breadth first search (BFS) on the network. Here is my code to do this so far: # Loads the igraph package library(igraph) # Creates a random (Erdos-Renyi) network with 100 nodes and edges with p = 0.2 graph <- erdos.renyi.game(100, 0.2, type = c("gnp", "gnm"), directed = FALSE, loops = FALSE) # Sets the attributes of all the

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, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L)), .Names = c("vertex", "affilation"), class = "data.frame", row