How to plot a bipartite graph in R

Question

How do I plot a network of type bipartite in R? Similar to this:

I have similar data but with weights for both genes and diseases and SARS. This network is an example. I have different kind of attributes. I followed a link here. But due to my little knowledge in this topic, I could not get much out of it. Thanks in advance for any help.

Answer 1

From the ?bipartite_graph help:

Bipartite graphs have a type vertex attribute in igraph, this is boolean and FALSE for the vertices of the first kind and TRUE for vertices of the second kind.

So you could do something like this (igraph 1.0.1):

library(igraph)

set.seed(123)

# generate random bipartite graph.
g <- sample_bipartite(10, 5, p=.4)
# check the type attribute:
V(g)$type

# define color and shape mappings.
col <- c("steelblue", "orange")
shape <- c("circle", "square")

plot(g,
  vertex.color = col[as.numeric(V(g)$type)+1],
  vertex.shape = shape[as.numeric(V(g)$type)+1]
)

Check also ?bipartite.

---

Using the example provided by the OP in the comments. Since the graph is multipartite and given the provided data format, I would first create a bipartite graph, then add the additional edges. Note that although the resulting graph returns TRUE for is_bipartite() the type argument is specified as numeric instead of logical and may not work properly with other bipartite functions.

set.seed(123)
V1 <- sample(LETTERS[1:10], size = 10, replace = TRUE)
V2 <- sample(1:10, size = 10, replace = TRUE)

d <- data.frame(V1 = V1, V2 = V2, weights = runif(10))
d
> d
   V1 V2   weights
1   C 10 0.8895393
2   H  5 0.6928034
3   E  7 0.6405068
4   I  6 0.9942698
5   J  2 0.6557058
6   A  9 0.7085305
7   F  3 0.5440660
8   I  1 0.5941420
9   F  4 0.2891597
10  E 10 0.1471136

g <- graph_from_data_frame(d, directed = FALSE)
V(g)$label <- V(g)$name # set labels.

# create a graph connecting central node FOO to each V2.
e <- expand.grid(V2 = unique(d$V2), V2 = "FOO")
  > e
  V2  V2
1 10 FOO
2  5 FOO
3  7 FOO
4  6 FOO
5  2 FOO
6  9 FOO
7  3 FOO
8  1 FOO
9  4 FOO

g2 <- graph.data.frame(e, directed = FALSE)

# join the two graphs.
g <- g + g2

# set type.
V(g)$type <- 1
V(g)[name %in% 1:10]$type <- 2
V(g)[name %in% "FOO"]$type <- 3

V(g)$type
> V(g)$type
 [1] 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3

col <- c("steelblue", "orange", "green")
shape <- c("circle", "square", "circle")

library(rTRM) # Bioconductor package containing layout.concentric()
# the fist element in the list for concentric is the central node.
l <- layout.concentric(g, concentric = list("FOO", 1:10, LETTERS[1:10]))
plot(g,
     layout = l,
     vertex.color = col[V(g)$type],
     vertex.shape = shape[V(g)$type],
     edge.width = E(g)$weights * 5 # optional, plot edges width proportional to weights.
)

The function layout.concentric() is in (my) package rTRM, available from Bioconductor. It is really a simple implementation I wrote to do exactly what you want. I am not completely sure whether the latest igraph version has the same functionality though (it may be).

Answer 2

For the example you provided, I would recommend using the x and y attributes for visualizing a bipartite graph. E.g.:

V(g)$x <- c(1, 1, 1, 2, 2, 2, 2)
V(g)$y <- c(3, 2, 1, 3.5, 2.5, 1.5, 0.5)
V(g)$shape <- shape[as.numeric(V(g)$type) + 1]
V(g)$color <- c('red', 'blue', 'green', 'steelblue', 'steelblue', 'steelblue', 'steelblue')
E(g)$color <- 'gray'
E(g)$color[E(g)['A' %--% V(g)]] <- 'red'
E(g)$color[E(g)['B' %--% V(g)]] <- 'blue'
E(g)$color[E(g)['C' %--% V(g)]] <- 'green'
plot(g)

EDIT: added code to give the vertices and edges different colors for clarity.