Gossip: Identifying Central Individuals in a Social Network

Abhijit Banerjee, Matthew Jackson, Esther Duflo and Arun G. Chandrasekhar

No 10120, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Can we identify the members of a community who are best- placed to diffuse information simply by asking a random sample of in- dividuals? 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.

Keywords: Centrality; Diffusion; Gossip; Influence; Networks; Social learning (search for similar items in EconPapers)
JEL-codes: D13 D85 L14 O12 Z13 (search for similar items in EconPapers)
Date: 2014-08
New Economics Papers: this item is included in nep-cta, nep-net and nep-soc
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Citations: View citations in EconPapers (55)

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Related works:
Working Paper: Gossip: Identifying Central Individuals in a Social Network (2014)
Working Paper: Gossip: Identifying Central Individuals in a Social Network (2014)
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