igraph

Trying to find communities in tweet data. The cosine similarity between different words forms the adjacency matrix. Then, I created graph out of that adjacency matrix. Visualization of the graph is the task here: # Document Term Matrix dtm = DocumentTermMatrix(tweets) ### adjust threshold here dtms = removeSparseTerms(dtm, 0.998) dim(dtms) # cosine similarity matrix t = as.matrix(dtms) # comparing two word feature vectors #cosine(t[,"yesterday"], t[,"yet"]) numWords = dim(t)[2] # cosine measure between all column vectors of a matrix. adjMat = cosine(t) r = 3 for(i in 1:numWords) { highElement

I am trying to implement a " Minimum Cost Network Flow " transportation problem solution in R . I understand that this could be implemented from scratch using something like lpSolve . However, I see that there is a convenient igraph implementation for " Maximum Flow ". Such a pre-existing solution would be a lot more convenient, but I can't find an equivalent function for Minimum Cost. Is there an igraph function that calculates Minimum Cost Network Flow solutions, or is there a way to apply the igraph::max_flow function to a Minimum Cost problem? igraph network example: library(tidyverse)

I am using igraph (Python) and would like to get all possible paths between two nodes in a directed graph. I am aware of the function get_all_shortest_paths , which is for shortest paths, but could not find a general one. Update: My main goal is to get all nodes in these paths, so that I can then get a subgraph of these nodes. Since you mentioned in your question that your ultimate goal is to get only the nodes that are in these paths and not the paths themselves, I think you don't even have to calculate the paths. The Graph object in igraph has a method called subcomponent . By default, it

I'm working with igraph for R. My graph is based on an edgelist which includes parallel edges (more than one edge with the same source and target). I would like to convert these parallel edges to an edge attribute weight. Is there an eay way to do this? If there is no easy way. how can I identify these parallel edges? duplicated(E(net)) does not return a single duplicate. I suppose its looking for duplicated edge ids. You can also use E(graph)$weight <- 1 followed by simplify(graph, edge.attr.comb=list(weight="sum")) to assign a weight of 1 to each edge and then collapsing multiple edges into

I would like to detect overlapping communities in small networks/graphs. By overlapping, I mean that a node can be included within more than one communities/clusters in the output of the detection algorithm. I have looked at various community detection algorithms curretly provided by igraph , but I think none of them handles overlapping communities. Ideally, I would like to be able to programmatically utilize some implementation of such algorithm(s) in Python. However, implementation in other languages is OK too. I have implemented the hierarchical link clustering algorithm of Ahn et al a

I have the following network diagram: set.seed(1410) df<-data.frame( "site.x"=c(rep("a",4),rep("b",4),rep("c",4),rep("d",4)), "site.y"=c(rep(c("e","f","g","h"),4)), "bond.strength"=sample(1:100,16, replace=TRUE)) library(igraph) df<-graph.data.frame(df) V(df)$names <- c("a","b","c","d","e","f","g","h") layOUT<-data.frame(x=c(rep(1,4),rep(2,4)),y=c(4:1,4:1)) E(df)[ bond.strength < 101 ]$color <- "red" E(df)[ bond.strength < 67 ]$color <- "yellow" E(df)[ bond.strength < 34 ]$color <- "green" V(df)$color <- "white" l<-as.matrix(layOUT) plot(df,layout=l,vertex.size=10,vertex.label=V(df)$names,

I am trying to plot small networks using igraph in R. Each vertex in the network has a name, which is equivalent to its label. I would like to make each vertex have a rectangular symbol that is just large enough to fit its label. This is my main inspiration. What is the best way to do this with igraph? Edit: more information The code is here jsonToNM <- function(jfile, directed=TRUE) { # Requires "rjson" and "igraph" nm.json <- fromJSON(file=jfile) nm.graph <- c() # Initialize the graph with the given nodes g <- graph.empty(n=length(nm.json), directed=directed) # Add their names V(g)$name <-