This little function calculates Degree Centrality (CD) for each node in an undirected simplified graph. The node column also has to be entered as data frame. Then you get it to work like this… And that should return something like

## The Full Graphing Monty – R

Spent the entire day bashing out graphs (not a euphemism at all). I did some pretty intense node analysis, and then some plotting. This what I used for plotting This is the final result — one of the smaller graphs. Just as a final note. I freaking LOVE MacVim.

## Calculating Degree Centrality – R

Degree Centrality measures how connected an actor is within a social group. A very simple way of calculating this is by simply counting the number of connection, where you end up with an integer like 4 or 12. Integers, however, don’t really work when you’re trying to compare Degree Centrality between actors in two social […]

## Activity in 4.x Development – R

EDIT: Here is a more up-to-date entry The objective is to draw a graph and have each node coloured according to its centrality using R. I’m starting with a large-ish data frame — a 1,430 long edge list. First thing is to make sure we’ve loaded the igraph library. Then we load the edge list […]

## From Edge List to User List using R

The objective is to turn an edgelist into a user list, and then apply Bradford’s Law from my previous post. My raw data is in edge list format, where each row represents an interaction between two community members (actors), and the actor names are separated by a single space, like this So here we have […]

## The People in the Middle – R

Following from my long theoretical post about the people in the middle, I now need to split a very long list of users into three groups, according to the number of posts (frequency), so that each groups is responsible for 1/3 of posts. In other words, group one will be responsible for percentiles 0% to […]

So today I’m sorting out the list of users according to Bradford’s Law of distribution, which mainly consists of dividing the lists into three groups, where each group is responsible for 33% of posts. The idea is that the first group, made up of those who post the most, will be small. Then the second […]

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