Archive for the ‘igraph’ Category

Network visualization in R with the igraph package

Tuesday, November 6th, 2012

Network visualization in R with the igraph package by Dimiter Toshkov.

From the post:

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.

If you find the igraph package documentation suboptimal, this will give you a leg up on using the package.

Impressive results await you.

Two of the more important reasons to use network visualization as an exploration tool for data:

  1. To construct a useful network visualization you have to slow down and carefully consider the data and the relationships you want to represent. Simply “knowing” the data better, whatever technique helps you slow down to do that, is a good one.
  2. The visualization itself may help you see relationships that are missing or relationships that were unexpected. Either clearing out your assumptions about the data or reducing the noise level in the data



igraph and structured text exploration

Monday, July 2nd, 2012

igraph and structured text exploration

From the post:

I am in the slow process of developing a package to bridge structured text formats (i.e. classroom transcripts) with the tons of great R packages that visualize and analyze quantitative data (If you care to play with a rough build of this package (qdap) see: One of the packages qdap will bridge to is igraph.

A while back I came across a blog post on igraph and word statistics (LINK). It inspired me to learn a little bit about graphing and the igraph package and provided a nice intro to learn. As I play with this terrific package I feel it is my duty to share my experiences with others who are just starting out with igraph as well. The following post is a script and the plots created with a word frequency matrix (similar to a term document matrix from the tm package) and igraph:

A very nice introduction to the use of igraph for exploring texts.

igraph 0.6 Release

Saturday, June 30th, 2012

igraph 0.6 Release

From the introduction:

igraph is a free software package for creating and manipulating undirected and directed graphs. It includes implementations for classic graph theory problems like minimum spanning trees and network flow, and also implements algorithms for some recent network analysis methods, like community structure search.

The efficient implementation of igraph allows it to handle graphs with millions of vertices and edges. The rule of thumb is that if your graph fits into the physical memory then igraph can handle it.

OK, I’m packing n x GB of RAM, so should be able to do some serious damage.

There are too many changes, features and fixes to easily summarize them. See Release Notes 0.6.

Documentation for igraph is available for R, C and Python interfaces. Features are not always the same across interfaces.

I mention that because in this release, in the R interface, vertexes and edges are numbered from one. For C and Python, vertexes and edges continue to be counted from zero.

An Example of Social Network Analysis with R using Package igraph

Sunday, May 20th, 2012

An Example of Social Network Analysis with R using Package igraph by Yanchang Zhao.

From the post:

This post presents an example of social network analysis with R using package igraph.

The data to analyze is Twitter text data of @RDataMining used in the example of Text Mining, and it can be downloaded as file “termDocMatrix.rdata” at the Data webpage. Putting it in a general scenario of social networks, the terms can be taken as people and the tweets as groups on LinkedIn, and the term-document matrix can then be taken as the group membership of people. We will build a network of terms based on their co-occurrence in the same tweets, which is similar with a network of people based on their group memberships.

I like the re-use of traditional social network analysis with tweets.

And the building of a network of terms based on co-occurrence.

May or may not serve your purposes but:

If you don’t look, you won’t see.

Basic graph analytics using igraph

Tuesday, May 1st, 2012

Basic graph analytics using igraph by Ricky Ho.

From the post:

Social Network Site such as Facebook, Twitter becomes are integral part of people’s life in. People interact with each other in different form of activities and a lot of information has been captured in the social network. Mining such a network can reveal some very useful information that can help an organization to gain competitive advantages.

I recently come across a powerful tools called igraph that provides some very powerful graph mining capabilities. Following are some interesting things that I have found.

Ricky doesn’t give a link to igraph, which you can find here. Development version.

He does cover:

  • Create a Graph
  • Basic Graph Algorithms
  • Graph Statistics
  • Centrality Measures

Network Analysis with igraph

Thursday, March 29th, 2012

Network Analysis with igraph by Gábor Csárdi.

I saw this mentioned on Christophe Lalanne’s Bag of Tweets for March 2012 and wanted to insert a word of caution.

While it is true that the igraph documentation page also points to the “Network Analysis with igraph” page as being “under development,” a number of sections are not done at all.

The most recent copyright date is 2006.

Just be aware that igraph 0.3.2 was released in December of 2006. The latest version of igraph is 0.5.4, released in August of 2010.

For your convenience: igraph at Sourceforge, development version at Launchpad.