I was asked recently what I thought about research in Social Networks (SN). The correct response to a question of this variety when asked in public before an audience that contains scholars thinking about this subject is to say something noncommital or perhaps imitate Herman Cain on Libya. I did neither. I said what was on my mind and upon reflection, I realize it was not the best answer. With the benefit of time, I think have a better answer (and no, I’m not about to mimic Romney either!).
There are, I think, 3 strands in the literature dubbed SN. The first is the use of information contained in the web of relationships an individual maintains to make inferences about that individual. In one sense this is a standard statistical (or machine learning) exercise but with the wrinkle that the potentially relevant independent variables are associated with properties of the network within which the individual is embedded. The second strand is about how infection or ideas spread through a network. This is a subject that is at least 20 years old (see for example the 1988 survey paper by Hedetniemi, Hedetniemi and Liestman). The new wrinkle here has been the desire to study diffusion in networks that resemble what is observed rather than networks in general. Thus, I would also lump into this strand of the literature the work done to document and describe observed networks (eg the power law stuff).
The third strand is driven by the idea that one’s place in the network determines one’s influence, power or wealth. It is this strand of the literature that I am skeptical about. Note, skeptical not opposed to. First, I accept that `who you know’ matters. But if this is the extent of the insight such models yield, it is thin gruel. Now, there are models that seek to show how the structure of the network influences the distribution of wealth, say, These usually involve an exogenously given static network. However, one would suspect that such networks are really dynamic and the products of strategic choices of the agents. Thus, these models are still too distant from reality to be compelling. Suggestive, perhaps, but not compelling.