reunion latinoamericana de analisis de redes sociales

Redes de consejo y difusión local de innovaciones tecnológicas.

Juan Carlos Barahona. MIT Media Laboratory.
Massachusetts Institute of Technology. USA.1


Finding the influential people in a community is key to diffusion process of technological innovations, as well as other kinds of products. The ability to recognize who are the influential members of a community is important for diffusion policy makers and managers. This information is traditionally obtained through costly ethnographic studies which are not necessarily efficient. In certain endeavors the use of socioeconomic and demographic measures characteristic of those ethnographic studies is not effective, because the target population is very homogeneous. In the specific case of diffusion of advanced digital technologies in underserved communities or rural areas the challenge of economic sustainability becomes an issue and the cost of traditional methods to find who are the influential members becomes prohibitive.

We explore the use of sociometric information as a supplement to socioeconomic and demographic variables to determine the influential members of a community, under conditions where conventional methods may fail. We believe that identifying the structural characteristics of the flow of advice plays a key role in this space. We explore the theoretical possibilities of different possible graph-theoretic measures given data about networks.

An empirical study of these ideas using data on a community of Costa Rican coffee growers is reported. We collected sociometric data from 122 producers and compare our results with an independent ethnographic study of the same population.  It turns out that the flow of advice captured by a generalized measure of eigenvector centrality, controlling for age and innovativeness using a logistic regression method, produced a good predictor of the influential members of the community. In terms of the positive predicting value our results suggest that we can double the precision (for this particular data set we got 91.66% vs. 45% obtained by the conventional methods).

Sociometric data is expected to become more available and easier to record and process, as mobile phones, computers of all sizes and Internet become ubiquitous and better algorithms for data mining from those devices evolve. This work is part of a larger research agenda aimed at designing methods and applications informed by the structural properties of human dynamics to improve the flow of ideas and innovations.



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Ier. Reunión Latinoamericana de Análisis de Redes Sociales.
Agosto de 2007, La Plata, Argentina.
Univ. Nacional de La Plata, 2008. ISBN 978-950-34-0513-0