Getting Started with rnetcarto
## Loading required package: igraph##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionRnetcarto in 60 seconds.
Rnetcarto provides fast network modularity and roles computation by simulated annealing (rgraph C library 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:
# 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)## [[1]]
## name module connectivity participation role
## 5 d 0 -1.5689291 0.0000000 Ultra peripheral
## 3 b 0 -0.5883484 0.0000000 Ultra peripheral
## 2 b 0 0.3922323 0.0000000 Ultra peripheral
## 8 h 0 0.3922323 0.0000000 Ultra peripheral
## 7 g 0 1.3728129 0.3200000 Peripheral
## 1 a 1 1.0000000 0.0000000 Ultra peripheral
## 6 e 1 1.0000000 0.0000000 Ultra peripheral
## 4 c 1 1.0000000 0.4444444 Peripheral
##
## [[2]]
## [1] 0.2520661Input: 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
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)## A B C
## A 0 1 1
## B 1 0 1
## C 1 1 0Note that igraph package can be used to manipulate and
plot graphs:
# import from rnetcarto matrix format to igraph:
G = igraph::graph.adjacency(input,weighted=TRUE,mode="undirected")## Warning: `graph.adjacency()` was deprecated in igraph 2.0.0.
## ℹ Please use `graph_from_adjacency_matrix()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: `get.adjacency()` was deprecated in igraph 2.0.0.
## ℹ Please use `as_adjacency_matrix()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Example 2: Two triplets
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:
## A B C D E F
## A 0 10 10 0 0 0
## B 10 0 10 0 0 0
## C 10 10 0 0 0 0
## D 0 0 0 0 10 10
## E 0 0 0 10 0 10
## F 0 0 0 10 10 0
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).
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)## Team 1 Team 2
## A 1 0
## B 1 0
## C 1 0
## D 0 1
## E 0 1
## F 0 1List 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:
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))## [[1]]
## [1] "A" "B" "C" "D" "E" "F" "C"
##
## [[2]]
## [1] "B" "C" "A" "E" "F" "D" "D"Example 2: Weighted network
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)## [[1]]
## [1] "A" "B" "C" "D" "E" "F" "C" "A"
##
## [[2]]
## [1] "B" "C" "A" "E" "F" "D" "D" "D"
##
## [[3]]
## [1] 10 10 10 10 10 10 10 10 1Example 3: Bipartite network
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)## [[1]]
## [1] "A" "B" "C" "D" "E" "F" "C" "A"
##
## [[2]]
## [1] "Team1" "Team2" "Team1" "Team1" "Team2" "Team1" "Team1" "Team2"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
## [[1]]
## name module connectivity participation role
## 1 A 0 0 0 Ultra peripheral
## 2 B 0 0 0 Ultra peripheral
## 3 C 0 0 0 Ultra peripheral
## 4 D 1 0 0 Ultra peripheral
## 5 E 1 0 0 Ultra peripheral
## 6 F 1 0 0 Ultra peripheral
##
## [[2]]
## [1] 0.5Example 2: Bipartite network
## [[1]]
## name module connectivity participation role
## 4 D 0 -0.5773503 0.0000000 Ultra peripheral
## 6 F 0 -0.5773503 0.0000000 Ultra peripheral
## 1 A 0 -0.5773503 0.4444444 Peripheral
## 3 C 0 1.7320508 0.0000000 Ultra peripheral
## 2 B 1 0.0000000 0.5000000 Peripheral
## 5 E 1 0.0000000 0.5000000 Peripheral
##
## [[2]]
## [1] 0.3317308