Network analysis: Where are you in my social network?

Michael Slater-Townshend talks extensively about the merits of understanding your own and other online communities in this in this months Royal Statistical Society magazine. After following his advice, I have discovered that it is surprisingly easy to download your own Facebook data and see which of your friends form connected groups.

Several apps allow you to download 'raw' Facebook data in a format that suits almost any statistical package. I used NameGenWeb. The resulting file can then be imported into a variety of statistical packages. I chose to use Gelphi for this example.

My unprocessed Facebook network looks like this...

Each dot (or node) is a friend and the lines show friendship connections between each individual. 

In order to make things manageable, I ran a cluster analysis to look for groups of people who are more connected to each other. This quickly produced three distinct groups. The larger circles represent clusters of 3 or more people who share many connections. 

The dark purple, yellow and pink nodes are difficult to categorize because they don't fit well into any of the defined groups. Those who sit in the middle could be described as being at the epicentre of my [Facebook] existence because they have strong links with all three clusters. Each individual can be identified from this model, but I opted to remove the name tags for clarity.

Of course this network is virtual and constantly changing as it relies on the behavioural patterns of nearly 350 individual data points. For many people, the resulting network may not reflect their real life social interaction. For example, I almost never see people in the school cluster with the exception of one large orange circle. This shows a minority of 3 close friends from school who I have continue to socialise with on a regular basis. 

There are many applications for this type of analysis, particularly when it comes to comparing real life and online social interaction. Other research has started to suggest that Facebook and Twitter status updates may also help predict personality. How these networks change and evolve over time (assuming Facebook is still around in 20 years) would presumably give a valuable insight into how friendship groups change as we age.

As you can probably imagine, the vast amounts of information have already become a valuable source of information for any future employer or recruitment agency! 


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