Network visualization in R with the igraph package

In this post I showed a visualization of the organizational network of my department. Since several people asked for details how the plot has been produced, I will provide the code and some extensions below. The plot has been done entirely in R (2.14.01) with the help of the igraph package. It is a great package but I found the documentation somewhat difficult to use, so hopefully this post can be a helpful introduction to network visualization with R. Here we go:

# Load the igraph package (install if needed)


# Data format. The data is in 'edges' format meaning that each row records a relationship (edge) between two people (vertices).
# Additional attributes can be included. Here is an example:
#	Supervisor	Examiner	Grade	Spec(ialization)
#	AA		BD		6	X	
#	BD		CA		8	Y
#	AA		DE		7	Y
#	...		...		...	...
# In this anonymized example, we have data on co-supervision with additional information about grades and specialization. 
# It is also possible to have the data in a matrix form (see the igraph documentation for details)

# Load the data. The data needs to be loaded as a table first: 

bsk<-read.table("", sep='t', dec=',', header=T)#specify the path, separator(tab, comma, ...), decimal point symbol, etc.

# Transform the table into the required graph format:<, directed=F) #the 'directed' attribute specifies whether the edges are directed
# or equivelent irrespective of the position (1st vs 2nd column). For directed graphs use 'directed=T'

# Inspect the data:

V( #prints the list of vertices (people)
E( #prints the list of edges (relationships)
degree( #print the number of edges per vertex (relationships per people)

# First try. We can plot the graph right away but the results will usually be unsatisfactory:

Here is the result:

Not very informative indeed. Let’s go on:

#Subset the data. If we want to exclude people who are in the network only tangentially (participate in one or two relationships only)
# we can exclude the by subsetting the graph on the basis of the 'degree':

bad.vs<-V([degree(<3] #identify those vertices part of less than three edges<-delete.vertices(, bad.vs) #exclude them from the graph

# Plot the data.Some details about the graph can be specified in advance.
# For example we can separate some vertices (people) by color:

V($color<-ifelse(V($name=='CA', 'blue', 'red') #useful for highlighting certain people. Works by matching the name attribute of the vertex to the one specified in the 'ifelse' expression

# We can also color the connecting edges differently depending on the 'grade': 

E($color<-ifelse(E($grade==9, "red", "grey")

# or depending on the different specialization ('spec'):

E($color<-ifelse(E($spec=='X', "red", ifelse(E($spec=='Y', "blue", "grey"))

# Note: the example uses nested ifelse expressions which is in general a bad idea but does the job in this case
# Additional attributes like size can be further specified in an analogous manner, either in advance or when the plot function is called:

V($size<-degree( the size of the vertices is specified by the degree of the vertex, so that people supervising more have get proportionally bigger dots. Getting the right scale gets some playing around with the parameters of the scale function (from the 'base' package)

# Note that if the same attribute is specified beforehand and inside the function, the former will be overridden.
# And finally the plot itself:
par(mai=c(0,0,1,0)) 			#this specifies the size of the margins. the default settings leave too much free space on all sides (if no axes are printed)
plot(,				#the graph to be plotted
layout=layout.fruchterman.reingold,	# the layout method. see the igraph documentation for details
main='Organizational network example',	#specifies the title
vertex.label.dist=0.5,			#puts the name labels slightly off the dots
vertex.frame.color='blue', 		#the color of the border of the dots 
vertex.label.color='black',		#the color of the name labels
vertex.label.font=2,			#the font of the name labels
vertex.label=V($name,		#specifies the lables of the vertices. in this case the 'name' attribute is used
vertex.label.cex=1			#specifies the size of the font of the labels. can also be made to vary

# Save and export the plot. The plot can be copied as a metafile to the clipboard, or it can be saved as a pdf or png (and other formats).
# For example, we can save it as a png:
png(filename="org_network.png", height=800, width=600) #call the png writer
#run the plot #dont forget to close the device
#And that's the end for now.

Here is the result:

Still not perfect, but much more informative and aesthetically pleasing.

Additional information can be found on this guide to igraph which is in development, the examples here, and the official CRAN documentation of the package. Especially useful is this list of the plot attributes that can be tweaked. The plots can also be adjusted interactively using the tkplot function instead of plot, but the options for saving the resulting figure are limited.

Have fun with your networks!

The hidden structure of (academic) organizations

All organizations have a ‘deep’ hidden structure based on the social interactions among its members which might or might not coincide with the official formal one. University departments are no exception – if anything, the informal alliances, affinities, and allegiances within academic departments are only too visible and salient.

Network analysis provides one way of visualizing and exploring the ‘deep’ organizational structure. In order to learn how to visualize small networks with R, I collected data on the social interactions within my own department and plugged the dataset in R (igraph package) to get the plot below. The figure shows the social network of my institute based on the co-supervision of student dissertations (each Master thesis has a supervisor who selects a so-called ‘second’ reader who reviews the draft and the two supervisors examine the student during the defence). So each link between nodes (people) is based on one joint supervision of a student. The total number of links (edges) is 264 which covers (approximately) all dissertations defended over the last year. In this version of the graph, the people are represented only by numbers but in the full version the actual names of people are plotted, the links are directional, and additional info (like the grade of the thesis) can be incorporated.

Altogether, the organization appears surprisingly well-integrated. Most ‘outsiders’ and most weakly-connected ‘islands’ are either occasional external readers, or new colleagues being ‘socialized’ into the organization. Obviously, some people are more ‘central’ in the sense of connecting to a more diverse set of people, while others serve as boundary-spanners reaching to people who would otherwise remain unconnected to the core.  I find the figure intellectually and aesthetically pleasing (given that it is generated with two lines of code) and perhaps a more thorough analysis of the network can be useful in organizational management as well.

New tool for discourse network analysis

EJPR has just published an article introducing a new tool for ‘discourse network analysis’. Using the tool, you can measure and visualize political discourses and the networks of actors affiliated to each discourse. One can study the actor congruence networks (based on the number of statements actors share), concept congruence networks (based on whether statements are used by an actor in the same way) and trace the evolution of both over time.

Here is a graph taken from the paper which illustrates the actor congruence networks for the issue of software patents in the EU (click to enlarge):

The discourse networks analysis tool is free and available from the website of Philip Leifeld, one of the co-authors of the article. I can’t wait to get my hands on the program and try it out for myself. The tool promises to be an interesting alternative to evolutionary factor analysis – another new method for studying policy frames and discourses that I recently discussed – with the added benefit of being able to present actors and frames in an integrated analysis.  

Here is the abstract of the EJPR article (there are more resources at this website):

In 2005, the European Parliament rejected the directive ‘on the patentability of computer-implemented inventions’, which had been drafted and supported by the European Commission, the Council and well-organised industrial interests, with an overwhelming majority. In this unusual case, a coalition of opponents of software patents prevailed over a strong industry-led coalition. In this article, an explanation is developed based on political discourse showing that two stable and distinct discourse coalitions can be identified and measured over time. The apparently weak coalition of software patent opponents shows typical properties of a hegemonic discourse coalition. It presents itself as being more coherent, employs a better-integrated set of frames and dominates key economic arguments, while the proponents of software patents are not as well-organised. This configuration of the discourse gave leeway for an alternative course of political action by the European Parliament. The notion of discourse coalitions and related structural features of the discourse are operationalised by drawing on social network analysis. More specifically, discourse network analysis is introduced as a new methodology for the study of policy debates. The approach is capable of measuring empirical discourses both statically and in a longitudinal way, and is compatible with the policy network approach.