This is an adaptation of a post I’d written around natural networks for my 'professional' blog (http://blogs.conchango.com/rizwantayabali). I reckon it has some relevance for us here too.
To start with, here are three books you should read
- Small World - Mark Buchanan
- Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life - Albert-Laszlo Barabasi
- Tipping Point – Malcolm Gladwell
Why now? Because with the improvements in business intelligence and analytics modelling, we can now really begin to understand and map online networks, and identify the ‘hubs’ or key people that we should be engaging with to turn them into advocates in order to drive take-up through word of mouth. With the ubiquity and popularity of blogs, reviews and the web, this area is turning into a marketing tool that should be taken extremely seriously. It might also help us figure out how a volunteer network might function and where we need to focus within it.
Complex networks like those involving people, although seemingly random, surprisingly do actually follow patterns that can be mapped, and essentially fall into two categories – small-world networks and scale free networks. If networks were linear i.e. A knows B, and B knows C, and so on... the link between A and Z would involve 26 steps; and any knowledge, opinion or influence Z might have would be pretty much inaccessible to A.
Small world networks however essentially describe a pattern of interconnectedness that involves a degree of randomness, i.e. maybe B also knows M and X, and maybe X knows Z, which dramatically improves the connectedness between A and Z. The originally studies in this area were carried out by Stanley Milgram who was responsible for identifying the phenomenon we now know as “6 degrees of separation”. Yes, it’s not a myth!
However real world natural networks do not work as simplistically as this. They have another property that’s even more crucial, known as preferential attachment. Preferential attachment is an example of a positive feedback cycle where initially random variations are automatically reinforced, thus greatly magnifying differences. In popular speak this is the 'Matthew effect' i.e. the rich get richer!
What this means is that the more connected something is, the more likely it is to gain new connections. In a social network this means that any new unconnected member is more likely to become acquainted with more visible members than with relative unknowns. These ‘visible’ elements are effectively hubs with lots of connections and therefore influence, and these networks show a pattern called the ‘Power law’, which basically means that doubling the number of hubs reduces the degrees of separation between elements in the network by a constant; in this case, our users.
In other words all our potential users are connected to one other, and although we all know this, so far I’ve not heard of anyone that’s really modelling this connectivity for the specific goal of building and improving online networks. Personally I'm fascinated by this area and reckon it’s part of the future of the web, which is why I'm keen to see if there's any way we can build some of this thinking into the way we set this project up.
If any of this interests you, check out what the specialists have to say on the network weaving blog online. It's pretty fascinating stuff even if it is a little geeky ;-)
