Blog post: http://traims.tumblr.com/post/143640774130/how-to-visualize-a-bipartite-graph

network_viz.r

library(igraph)		        # to work with graphs
library(RColorBrewer)    	# to use a color palette
library(plotrix) 	      	# to rescale variables


# Read the data
raw_data <- read.csv("network_data.csv")
names(raw_data) <- c("Source", "Target", "Count", "Money")

# reformat data for igraph library
# mark source and target vertices -> different "type"
vertices.source <- data.frame(unique(raw_data$Source))
vertices.target <- data.frame(unique(raw_data$Target))
vertices.source$type = TRUE
vertices.target$type = FALSE
names(vertices.source) <- c("name", "type")
names(vertices.target) <- c("name", "type")
vertices.target$name <- as.character(vertices.target$name)
vertices <- rbind(vertices.source, vertices.target)

# create a bipartite graph
g <- graph.data.frame(raw_data, directed=FALSE, vertices=vertices)

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

# set edge width depending on the deal amount
# - tune it, so that the differences would be visible on screen
E(g)$width <- log(E(g)$Money) - min(log(E(g)$Money)) + 1

# set edge color and shape depending on the type: source or target?
V(g)$color  = col[as.numeric(V(g)$type) + 1]
V(g)$shape = shape[as.numeric(V(g)$type) + 1]

# choose a color palette - this one is a 9-color palette called "YlOrRd"
palette <- brewer.pal( 9, "YlOrRd" )

plot(g,
  # tune how node labels are displayed
  vertex.label.cex=.7,
  # set edge color in a way that it would be visible
  edge.color = palette [ rescale ( log(E(g)$Count), range(4, 9) ) ]
)