Responding to my recent post about influencers in social networks (The anti-Malcolm Gladwell speaks), Rick Niederman at the Forsyth Institute sent some useful research references, saying "you may want to consider the work of Steve Borgatti (U of KY, school of business) and Tom Valente (USC medical school).... Steve has a recent Science article on this [and] Tom has 2 textbooks." Thanks, Rick.
Interesting stuff. I'm fascinated by graphs and social network analysis, especially how they pertain to dissemination of evidence and knowledge (graph theory was my favorite math course). So I looked up the Science article, Network Analysis in the Social Sciences. It's by Stephen P. Borgatti, Ajay Mehra, Daniel J. Brass, and Giuseppe Labianca (13-Feb-2009, pp 892-895). Their abstract says "For social scientists, the theory of networks has been a gold mine, yielding explanations for social phenomena in a wide variety of disciplines from psychology to economics. Here, we review the kinds of things that social scientists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field." I recommend this well-written article for anyone who wants a basic understanding of how network analysis is applied to social science.
Social capital and consequences. The authors note that a "fundamental axiom in social network research is that a node's position in a network determines in part the opportunities and constraints that it encounters, and in this way plays an important role in a node's outcomes. This is the network thinking behind the popular concept of social capital, which in one formulation posits that the rate of return on an actor's investment in their human capital (i.e., their knowledge, skills, and abilities) is determined by their social capital (i.e., their network location)."
[Fig. 4. Two illustrative ego networks. The one on the left contains many structural holes; the one on the right contains few. From Science, Network Analysis in the Social Sciences.]
Theoretical mechanisms and egos. The article reviews the current thinking on what makes things -- money, an idea, or something else -- flow from one node to another. "Perhaps the most common mechanism for explaining consequences of social network variables is some form of direct transmission from node to node." Researchers have examined various mechanisms, including adaptation, binding, and exclusion. Take the binding mechanism, for instance (it reminds me of the concept of connectedness in graph theory). "The idea is that social ties can bind nodes together in such a way as to construct a new entity whose properties can be different from those of its constituent elements. Binding is one of the mechanisms behind the popular notion of the performance benefits of 'structural holes'. Given an ego-network (the set of nodes with direct ties to a focal node, called 'ego,' together with the set of ties among members of the ego network), a structural hole is the absence of a tie among a pair of nodes in the ego network. A well-established proposition in social network analysis is that egos with lots of structural holes are better performers in certain competitive settings. The lack of structural holes around a node means that the node's contacts are 'bound' together—they can communicate and coordinate so as to act as one, creating a formidable 'other' to negotiate with. This is the basic principle behind the benefits of worker's unions and political alliances. In contrast, a node with many structural holes can play unconnected nodes against each other, dividing and conquering."
Is there a divide-and-conquer strategy for evidence-based medicine? Network analysis has implications for evidence-based medicine and other efforts to make change happen by disseminating knowledge: Should we be finding the structural holes in the network and distributing the evidence there?
[Fig 1. From Science, Network Analysis in the Social Sciences.]
By the way, the article begins with a nice historical recap that includes Jacob Moreno's 1932 'sociometry' study of girls running away from school (see above).
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