Getting Started with rnetcarto

In rnetcarto: Fast Network Modularity and Roles Computation by Simulated Annealing (Rgraph C Library Wrapper for R)

``` {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)

Input: How should I format my data?

The netcarto function can read network in either adjacency matrix or adjacency list format.

Matrix 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:

Example 1: Triplet

``` {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:

• Empty columns and lines are removed (Here G).
• If the matrix is not symmetric, symmetry will be enforced by taking 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")

Example 3: Bipartite triplets

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))

Example 2: Weighted network

``` {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)

Output: How should I read the result?

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.

Example 1: Weighted network

``` {r, echo=TRUE} netcarto(igraph::get.adjacency(G,sparse=FALSE))

### Example 2: Bipartite network


``` {r, echo=TRUE}
   netcarto(bipartite, bipartite=TRUE)

rnetcarto documentation built on Jan. 17, 2023, 1:12 a.m.