Gossip: Identifying Central Individuals in a Social Network

Abhijit Banerjee, Arun Chandrasekhar, Esther Duflo and Matthew Jackson

Working Papers from eSocialSciences

Abstract: The paper examines individuals’ abilities to identify the highly central people in their social networks, where centrality is defined by diffusion centrality (Banerjee, Chandrasekhar, Duflo, and Jackson, 2013), which characterizes a node’s influence in spreading information. It first show that diffusion centrality nests standard centrality measures – degree, eigenvector and Katz-Bonacich centrality – as extreme special cases. Next, it shows that boundedly rational individuals can, simply by tracking sources of gossip, identify who is central in their social network in the specific sense of having high diffusion centrality.

Keywords: Centrality; Gossip; Networks; Diffusion; Influence; Social Learning; Social networks; Diffusion Centrality; Standard Centrality Measures; Sources of Gossip. (search for similar items in EconPapers)
Date: 2014-06
Note: Institutional Papers
References: Add references at CitEc
Citations: View citations in EconPapers (57)

Downloads: (external link)
http://www.esocialsciences.org/Download/repecDownl ... &AId=5925&fref=repec

Related works:
Working Paper: Gossip: Identifying Central Individuals in a Social Network (2014)
Working Paper: Gossip: Identifying Central Individuals in a Social Network (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ess:wpaper:id:5925

Access Statistics for this paper

More papers in Working Papers from eSocialSciences
Bibliographic data for series maintained by Padma Prakash (padmaprakash@esocialsciences.com).