Visualize and Publish Your Graph in 1 Minute: A Quick Guide

Hey there! Are you tired of spending endless hours trying to visualize a graph quickly? May be you have found yourself wrestling to complex software or even resorting to photo editing tools in a bid to make your graphs presentable. Graph visualization shouldn’t be a headache, but often, the process can feel cumbersome and time-consuming. Whether you’re a data enthusiast, a researcher, or a professional in need of quick, polished visuals, the struggle to efficiently create and publish graphs is a common one. But fear not, as in the next few minutes, we’ll unravel the secrets to effortlessly creating and sharing your graphs in just a minute! From leveraging powerful tools to exploring handy tricks, let’s banish those graph-making woes together.

The best and easiest method I find is to use iGraph package(stands for “interactive graph”). Why to chose iGraph over other options like networkx specifically for publication purposes?

1. Rich Functionality: iGraph offers a wide range of functions and algorithms for graph analysis, manipulation, and visualization. It supports both directed and undirected graphs and provides various layout options, centrality measures, community detection algorithms, and more.
2. Efficiency: iGraph is written in C and optimized for performance, making it highly efficient for handling large-scale graphs. This efficiency is crucial when dealing with complex networks or big data.
3. Ease of Use: Despite its powerful capabilities, iGraph maintains a user-friendly interface. It’s relatively easy to learn, especially for users familiar with R or Python (preferably R), and provides clear documentation and examples for beginners.
4. Integration with R & Python: As an R package and Python module, iGraph seamlessly integrates with other R or Python libraries and statistical tools, allowing users to combine graph analysis with statistical modeling, data visualization, etc.
5. Cross-Platform Compatibility: iGraph is cross-platform, working on Windows, macOS, and Linux systems, providing consistency and flexibility across different operating systems.
6. Graph Visualization Capabilities: iGraph offers various visualization options, enabling users to customize the appearance of graphs according to their needs. It allows for the visualization of node attributes, edge attributes, and complex network structures.

Visualization in R

First of all you need an editor to write R code like RStudio, don’t worry if you don’t have RStudio installed on your system, Google colab is giving you a high five. To reproduce the result in google colab first go to your drive at your desired directory -> click on New -> click on More -> click on google colaboratory -> click on Runtime -> click on ‘change runtime type’ -> Select R and hit Save.

Google Colaboratory demonstration

Coding time…

# Install the iGraph library 
install.packages("igraph") 

# Include the iGraph package in working environment 
library(igraph) 

# Make a graph 
g <- graph(c(1,2,3,5,2,4,4,3,3,1),
           directed = T
           )
# Plot the graph 
plot(g, 
     vertex.color = "green",
     vertex.size = 50,
     edge.color = "red",
     )

In the above code snippet, c(1, 2, 3, 5, 2, 4, 4, 3, 3, 1): This part creates the edges of the graph. It represents pairs of vertices connected by edges. In this example, it defines edges between vertices 1-2, 2-3, 3-5, 2-4, 4-3, 3-4, and 3-1 which is passed in graph function from igraph package that creates a graph object and assigns it to variable g and directed = TRUE: this argument specifies whether the graph is directed or not. In this case, directed = TRUE indicates that the graph being created is a directed graph.
Next, the plot() function is called to generate a visual representation of the graph, setting the node color to green, node size to 50 units, and edge color to red. Executing the above line of code simply produces the below appealing graph :)

You can customize the above code in many ways for example changing the name of vertex, adding edge label, etc.

# Install the iGraph library 
install.packages("igraph") 

# Include the iGraph package in working environment 
library(igraph) 

# Make a graph 
g <- graph(c("John","Max","Max","Bob","Alice","Bob","Alex","John","Tony","John"),
           directed = T
)
# Plot the graph 
plot(g, 
     vertex.color = "green",
     vertex.size = 30,
     edge.color = "red",
)

Let’s build the network, using dataset. I am using the dataset of Amazon product purchase information.

Dataset information

Network was collected by crawling Amazon website. It is based on Customers Who Bought This Item Also Bought feature of the Amazon website. If a product i is frequently co-purchased with product j, the graph contains a directed edge from i to j.

The data was collected in March 02 2003.

You can find the dataset here download and unzip it, remove the unnecessary information from the text file. Your dataset in text file should look like:
FromNodeId ToNodeId
0 1
0 2
. .
Now lets start to make the visualization.

# Install the iGraph library 
install.packages("igraph") 

# Include the iGraph package in working environment 
library(igraph)

# Read data file
data <- read.table('{path to the directory of file}/Amazon0302.txt', header=T, sep='\t',nrows = 100)

# Convert the data into dataframe
y <- data.frame(data$FromNodeId, data$ToNodeId)

# Create network
net <- graph.data.frame(y, directed=F) # for convenience I am putting directed=F 

# Plot the Network
plot(net,
     vertex.color="green",
     edge.color="red") 

For the demonstration purpose and computational time complexity I am taking only 100 rows of data (nrows = 100), you can put larger number here. The network diagram looks like:

There are other information that you can derive using igraph are degrees of nodes (indegree, outdegree), diameter, edge density, reciprocity, betweenness, etc. You can find more information here.

Visualization in Python

I will show you, how you can leverage networkx library in python to quickly visualize your graph. Make sure if you are using google colab then runtime type should be python.

# install networkx library 
pip install networkx 

# import networkx 
import networkx as nx

# import matplotlib.pyplot
import matplotlib.pyplot as plt
 
# Create a graph
G = nx.Graph()

# Add nodes to the graph
G.add_nodes_from(['A', 'B', 'C', 'D'])

# Add edges to the graph
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'D')])

# Draw the graph
nx.draw(G, with_labels=True)

# Show the graph
plt.show()

Running the above line of codes gives the following plot:

Conclusion

In this concise guide, we’ve gone through the most basic yet efficient way of graph visualization, uncovering efficient techniques to swiftly create and share compelling graphs. With tools like iGraph and NetworkX, we’ve explored powerful libraries that simplify the complex task of graph manipulation, analysis, and visualization.

Further Reading

1. Barabási, A-L. (2002). Linked: The New Science of Networks. Harvard University Press.
2. Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press.
3. iGraph Documentation.
4. NetworkX Documentation.
5. Other free to use Network Data Sources: Link

Thanks for reading till end. Feel free to drop your feedback here !!