Gossip: Identifying Central Individuals in a Social Network

Abstract:
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.

Source: National Bureau of Economic Research

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Measuring ‘neighborhood’: Constructing network neighborhoods

 Abstract:

"This study attempts to measure neighborhood boundaries in a novel way by creating network neighborhoods based on the density of social ties among adolescents. We create valued matrices based on social ties and physical distance between adolescents in the county. We then perform factor analyses on these valued matrices to detect these network neighborhoods. The resulting network neighborhoods show considerable spatial contiguity. We assess the quality of these aggregations by comparing the degree of agreement among residents assigned to the same network neighborhood when assessing various characteristics of their “neighborhood”, along with traditional definitions of neighborhoods from Census aggregations. Our findings suggest that these network neighborhoods are a valuable approach for “neighborhood” aggregation."

Source: U.C. Irvine retrieved from eScholarship repository

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How Peer Pressure Shapes Consensus, Leadership, and Innovations in Social Groups

Abstract:

What is the effect of the combined direct and indirect social influences—peer pressure (PP)—on a social group's collective decisions? We present a model that captures PP as a function of the socio-cultural distance between individuals in a social group. Using this model and empirical data from 15 real-world social networks we found that the PP level determines how fast a social group reaches consensus. More importantly, the levels of PP determine the leaders who can achieve full control of their social groups. PP can overcome barriers imposed upon a consensus by the existence of tightly connected communities with local leaders or the existence of leaders with poor cohesiveness of opinions. A moderate level of PP is also necessary to explain the rate at which innovations diffuse through a variety of social groups.

Source: Nature, Scientific Reports

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