Information Flow in Social Groups

by: Fang Wu, Bernardo A Huberman, Lada A Adamic, Joshua Tyler

(22 May 2003)

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arXiv (abstract), arXiv (PDF)

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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.

@misc{citeulike:311564, 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.}, author = {Wu, Fang and Huberman, Bernardo A. and Adamic, Lada A. and Tyler, Joshua }, citeulike-article-id = {311564}, eprint = {cond-mat/0305305}, keywords = {networktheory, socialnetworks}, month = {May}, posted-at = {2007-07-16 19:52:29}, priority = {4}, title = {Information Flow in Social Groups}, url = {http://arxiv.org/abs/cond-mat/0305305}, year = {2003} }

RIS record

TY - ELEC ID - citeulike:311564 L3 - citeulike-article-id:311564 TI - Information Flow in Social Groups N2 - 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. KW - networktheory KW - socialnetworks AU - Wu, Fang AU - Huberman, Bernardo AU - Adamic, Lada AU - Tyler, Joshua PY - 2003/May/22/ UR - http://arxiv.org/abs/cond-mat/0305305 ER -

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