Information Flow in Social Groups

Preprint

23 May 2003

preprint
Published by arXiv in arXiv

https://doi.org/10.48550/arXiv.cond-mat/0305305

Abstract

We present a study of information flow that takes into account the observation that an item relevant to one person is more likely to be of interest to individuals in the same social circle than those outside of it. This is due to the fact that the similarity of node attributes in social networks decreases as a function of the graph distance. An epidemic model on a scale-free network with this property has a finite threshold, implying that the spread of information is limited. We tested our predictions by measuring the spread of messages in an organization and also by numerical experiments that take into consideration the organizational distance among individuals.

Keywords

INFORMATION FLOW
SOCIAL NETWORKS
EPIDEMIC MODEL
GRAPH DISTANCE
SOCIAL GROUPS
SOCIAL CIRCLE
CIRCLE THAN

All Related Versions

Version 1, 2003-05-14, ArXiv
Version 2, 2003-05-23, ArXiv
Published version: Physica A: Statistical Mechanics and its Applications, 337 (1-2), 327.

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