Real world natural networks do not work as simplistically as theoretical complex or small world networks. 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 believe it’s part of the future of the web; hence this series of posts.