Arun G. Chandrasekhar

TL;DR: This article examined how participation in a micro-finance program diffuses through social networks and found that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants.

TL;DR: This article developed a model of word-of-mouth diffusion and applied it to data on social networks and participation in a newly available micro-finance loan program in 43 Indian villages to study the impact of the choice of injection points in the diffusion of a new product in a society.

TL;DR: In this article, a model of semi-Bayesian learning on networks is proposed to predict how cross-village patterns of learning relate to network structure, which are borne out in the data.

TL;DR: It is shown that boundedly-rational individuals can, simply by tracking sources of gossip, identify those who are most central in a network according to "diffusion centrality," which nests other standard centrality measures.

TL;DR: A simple model of diffusion shows how boundedly rational individuals can, just by tracking gossip about people, identify those who are most central in a network according to diffusion centrality (a measure of network centrality which nests existing ones, and predicts the extent to which piece of information seeded to a network member diffuses in finite time).