``` {r, echo=FALSE, results="hide"} library(rnetcarto) require("igraph")
# Rnetcarto in 60 seconds. Rnetcarto provides fast network modularity and roles computation by simulated annealing ([rgraph C library](https://github.com/seeslab/rgraph) wrapper for R). It exposes one main command named `netcarto` that take a graph as an input (formatted as an **adjacency matrix** or **list**, as described in more detail below) and returns a partition of the graph optimizing a given modularity criterion. It also computes the modularity roles of the nodes. Here is a small example: ``` {r, echo=TRUE} # Generate a simple random network a = matrix(as.integer(runif(100)<.3), ncol=10) a[lower.tri(a)] = 0 rownames(a) = c('a','b','b','c','d','e','f','g','h','i') colnames(a) = rownames(a) # Find an optimal partition for modularity using netcarto. # The output consists in a table containing node properties, # and the modularity value of the partition. netcarto(a)
The netcarto
function can read network in either adjacency matrix or
adjacency list format.
square symmetric matrix. In this format, the weight $w$ of an between
If you choose the matrix format, your network must consist in a
vertices $i$ and $j$ is given by the corresponding value in the matrix
web[i,j]
. Auto-loop (i.e. diagonal terms are authorised). You may name the rows and/or
columns, those names will be used in the function output. Example:
``` {r, echo=TRUE} input = matrix(0,3,3) input[1,2] = 1 input[2,3] = 1 input[3,1] = 1 input[2,1] = 1 input[3,2] = 1 input[1,3] = 1 rownames(input) = c("A","B","C") colnames(input) = rownames(input) print(input)
Note that `igraph` package can be used to manipulate and plot graphs: ``` {r, echo=TRUE} # import from rnetcarto matrix format to igraph: G = igraph::graph.adjacency(input,weighted=TRUE,mode="undirected") # Export to a matrix compatible with netcarto: input = igraph::get.adjacency(G,sparse=FALSE)
``` {r, echo=FALSE} plot(G, layout = igraph::layout.circle, , vertex.size = 60, vertex.color="red", vertex.frame.color= "white", vertex.label.color = "white", vertex.label.family = "sans", edge.width=1, edge.color="black")
### Example 2: Two triplets ``` {r, echo=TRUE} input = matrix(0,7,7) input[1,2] = 10 input[2,3] = 10 input[3,1] = 10 input[4,5] = 10 input[5,6] = 10 input[6,4] = 10 rownames(input) = c("A","B","C","D","E","F","G") colnames(input) = rownames(input)
Note that:
G
).web = web+t(web)-diag(web)
So the previous matrix is equivalent to:
``` {r, echo=FALSE} input = matrix(0,6,6) input[1,2] = 10 input[2,3] = 10 input[3,1] = 10 input[4,5] = 10 input[5,6] = 10 input[6,4] = 10 input = input+t(input)-diag(input) rownames(input) = c("A","B","C","D","E","F") colnames(input) = rownames(input) print(input)
``` {r, echo=FALSE} G = igraph::graph.adjacency(input,weighted=TRUE,mode="undirected") plot(G, layout = layout.circle, , vertex.size = 60, vertex.color="red", vertex.frame.color= "white", vertex.label.color = "white", vertex.label.family = "sans", edge.width=1, edge.color="black")
Note that the matrix may not be square and symmetric if and only
if you are considering a bipartite network (using the bipartite
flag).
``` {r, echo=TRUE} input = matrix(0,6,2) input[1,1] = 1 input[2,1] = 1 input[3,1] = 1 input[4,2] = 1 input[5,2] = 1 input[6,2] = 1 rownames(input) = c("A","B","C","D","E","F") colnames(input) = c("Team 1", "Team 2") print(input)
## List format If you choose the **list format**, your network must be formatted as a R-list. The first element must be a vector giving the label. The third element is a vector of the edge weights. The weights are optional and are all set to one if the list contains only the first two elements. ### Example 1: Unweighted network: ``` {r, echo=TRUE} nd1 = c("A","B","C","D","E","F","C") nd2 = c("B","C","A","E","F","D","D") web = list(nd1,nd2,weights) print(list(nd1,nd2))
``` {r, echo=TRUE} nd1 = c("A","B","C","D","E","F","C","A") nd2 = c("B","C","A","E","F","D","D","D") weights = c(10,10,10,10,10,10,10,10,1) web = list(nd1,nd2,weights) print(web)
### Example 3: Bipartite network ``` {r, echo=TRUE} nd1 = c("A","B","C","D","E","F","C","A") nd2 = c("Team1","Team2","Team1","Team1","Team2","Team1","Team1","Team2") bipartite = list(nd1,nd2) print(bipartite)
The netcarto
command output a list. Its first element is a dataframe
giving the name module, connectivity, and participation coefficient for
each node of the input graph. The second element is the modularity
of this optimal partition.
``` {r, echo=TRUE} netcarto(igraph::get.adjacency(G,sparse=FALSE))
### Example 2: Bipartite network ``` {r, echo=TRUE} netcarto(bipartite, bipartite=TRUE)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.