Visualization of social networks in Stata using multidimensional scaling

Abstract: Agricultural information is transferred through social interactions; therefore, ties to agricultural informants and network structures within farmers’ local neighborhoods determine their information-gathering abilities. This paper uses a spatial autoregressive model that takes account of spatial autocorrelation to examine such network connections, including friendship networks and advice networks, upon farmers’ knowledge-gathering abilities during formal agricultural training. We found that peer advice networks are important to support knowledge-gathering activities, while friendship networks are not. Further examination of network structures confirms that farmers who occupy a central position in their local neighborhood networks are found to perform better in learning outcomes to some extent, indicating that local network position is positively related to problem-solving ability in an unknown environment outside their locale.

TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.