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':
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## 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
## 6 e 0 -1.4142136 0.625 Connector
## 9 i 0 0.7071068 0.000 Ultra peripheral
## 4 c 0 0.7071068 0.375 Peripheral
## 1 a 1 -1.4142136 0.000 Ultra peripheral
## 2 b 1 0.0000000 0.000 Ultra peripheral
## 5 d 1 0.0000000 0.000 Ultra peripheral
## 7 g 1 1.4142136 0.560 Peripheral
## 8 h 2 -1.0000000 0.000 Ultra peripheral
## 3 b 2 1.0000000 0.640 Connector
##
## [[2]]
## [1] 0.08398438Input: 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 per session.
## 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 per session.
## 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
## 4 D 0 0 0 Ultra peripheral
## 5 E 0 0 0 Ultra peripheral
## 6 F 0 0 0 Ultra peripheral
## 1 A 1 0 0 Ultra peripheral
## 2 B 1 0 0 Ultra peripheral
## 3 C 1 0 0 Ultra peripheral
##
## [[2]]
## [1] 0.5Example 2: Bipartite network
## [[1]]
## name module connectivity participation role
## 2 B 0 0.0000000 0.5000000 Peripheral
## 5 E 0 0.0000000 0.5000000 Peripheral
## 4 D 1 -0.5773503 0.0000000 Ultra peripheral
## 6 F 1 -0.5773503 0.0000000 Ultra peripheral
## 1 A 1 -0.5773503 0.4444444 Peripheral
## 3 C 1 1.7320508 0.0000000 Ultra peripheral
##
## [[2]]
## [1] 0.3317308