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Information Flow in Social Groups

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Fang Wu, Bernardo A. Huberman, Lada A. Adamic, and Joshua R. Tyler

Information Dynamics Laboratory, HP Labs


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.

Full paper in PDF format

Physica A, 337:327-335, 2004.

PowerPoint presentation (in PDF) (given at the annual CNLS conference on Networks, Santa Fe, NM, May 12, 2003)

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