Social Network Analysis – a quietly useful research approach

Social Network Analysis helps us understands systems, repertoires and peer influences that help drive our behaviours.

The first social network diagram was, I believe, drawn in 1935 by a sociologist Mareno, who was describing the interactions between a handful of people he was studying.

Decades later of course, we now have the computational power to describe networks not just of small groups, but larger groups – hundreds, thousands or, I suppose – millions, as we see in the network clouds that map the political blogosphere, or the social interest clouds that define the woolly landscape of Facebook membership.

Social Network Analysis isn’t hard to conduct and of course there are very good freewares available sufficient to make it quite easy for any MR professional to spend an afternoon getting themselves familiar with the possibilities. I’m surprised I don’t see the fruits of SNA everywhere. Why is it so useful?

The answer is simple. First: people are social and are influenced by peers. So robust studies (the Framingham study for example) demonstrate quite simply that smoking is not just an individual choice, but very much peer driven thing. Smokers, it turns out happen to live within networks of other smokers. Obesity follows a similar pattern. Put simply, if you are surrounded by large people, then large is your “normal.”  I would imagine this social network effect applies to brand usage (the new product that everybody in the book group was raving about) and other individual choices which – if you put your network glasses on – become a lot more peer-driven than anyone might guess.

So that’s the main reason for SNA. People are social.

But there’s a second reason also. We tend to view things in terms of repertoires, clusters and systems.  When I say that I prefer to avoid rush-hour, what I’m really saying is that I’m trying to avoid a whole system of issues that culminate in lengthy travel time.  If you ask me what fruit I buy each week, my answer isn’t simply based on my favourite fruits in ranked order, but by my belief that I need a balanced diet – citrus, apples, stone fruit and bananas and maybe kiwis.  I wouldn’t dream of a purchase without some citrus but also bananas.  I see my choices not as a collection of individual choices, but as system that gives me a balance of flavour, goodness and value. In fact what Carlo Magni and I did was use fruit purchase data to create a SNA based not on people, but on fruit in a typical fruitbowl – in Japan the “system view” is very different from the “system view” in my home country of New Zealand. Bar charts would not have helped us visualise this so clearly. SNA gave us a real insight into the working heuristics of the Tokyo fruit buyer. We could also understand how seldom-mentioned fruit (yummy persimmons) fitted into the larger system, and why certain types of fruit – through poor definition – have trouble “breaking into” the social network of the typical fruitbowl.

There are two more reasons for using SNA and I’ll touch on these very briefly.

Reason three is that SNA’s produce a plethora of measures you didn’t expect to get.  When you generate a diagram, as above, you also generate a number of statistics for each node (or individual) in the network. Two useful measures are:

1) Between-ness. The degree to which a player connects two or more quite disparate groups within the network. In an organisation there may be just a few people linking Silo 1 to Silo 2.

2: Eigenvector.  The degree to which a player is plugged in to the wider network.

The fourth reason for using SNA is clarity. Clients easily ‘get’ a social network diagram, and can easily see how products, or people might glue together – or be disconnected.

Of course they easily get it. They’re human.

2 thoughts on “Social Network Analysis – a quietly useful research approach

    1. Hi Mary – Ucinet is widely used, well documented and quite straightforward to get started with. The input data will consist, basically of rows and columns – so if there were 30 staff in an organisation, ad internal staff network would consist of 30 rows Anne, Bob, Chan, Dave etc…and 30 columns Anne, Bob, Chan etc so you could plot if Anne refers to Bob, or conversely if Bob refers to Anne.

      For a much wider social network you’re best to work with a limited number of fields otherwise you’ll get the 6-degrees effect where suddenly you have the entire planet virtually linked. Too much for my PC to handle.

      Another software looks prettier and is based on Excel – NodeXL. I find it a bit less versatile, but it looks damned good. Ucinet looks a bit previous generation – the graphic I used in the story was a Ucinet creation.

      Hope this helps.

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