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)