Gossip: Identifying Central Individuals in a Social Network
Can we identify the members of a community who are best- placed to diffuse information simply by asking a random sample of individuals? We show 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. Testing this prediction with data from 35 Indian villages, we find that respondents accurately nominate those who are diffusion central (not just those with many friends). Moreover, these nominees are more central in the network than traditional village leaders and geographically central individuals.
Ben Golub, Michael Dickstein and participants at various seminars/conferences provided helpful comments. Financial support from the NSF under grants SES-1156182 and SES-1155302, from the AFOSR and DARPA under grant FA9550-12-1-0411, and from ARO MURI under award No. W911NF-12-1-0509 is gratefully acknowledged. We thank Francisco Munoz for excellent research assistance. We are grateful to CMF at IFMR, Tanay Balantrapu, Gowri Nagraj, and Manaswini Rao. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.