Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy

Social networks have been used to understand how information flows through an organization as well as identifying individuals that appear to have control over this information flow. Such individuals are identified as being central nodes in a graph representation of the social network and have high...

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Autores principales: Rudolph, Larry, Zhenghao, Chen
Formato: Artículo
Lenguaje:English
Publicado: 2005
Materias:
Acceso en línea:http://hdl.handle.net/1721.1/30288
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author Rudolph, Larry
Zhenghao, Chen
author_facet Rudolph, Larry
Zhenghao, Chen
author_sort Rudolph, Larry
collection MIT
description Social networks have been used to understand how information flows through an organization as well as identifying individuals that appear to have control over this information flow. Such individuals are identified as being central nodes in a graph representation of the social network and have high "betweenness" values. Rather than looking at graphs derived from email, on-line forums, or telephone connections, we consider sequences of bipartite graphs that represent face-to-face meetings between individuals, and define a new metric to identify the information elite individuals. We show that, in our simulations, individuals that attend many meetings with many different people do not always have high betweenness values, even though they seem to be the ones that control the information flow.
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spelling mit-1721.1/302882019-04-11T07:03:43Z Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy Rudolph, Larry Zhenghao, Chen social networks universal hashing privacy information flow pervasive computing face-to-face meetings models location tracking Social networks have been used to understand how information flows through an organization as well as identifying individuals that appear to have control over this information flow. Such individuals are identified as being central nodes in a graph representation of the social network and have high "betweenness" values. Rather than looking at graphs derived from email, on-line forums, or telephone connections, we consider sequences of bipartite graphs that represent face-to-face meetings between individuals, and define a new metric to identify the information elite individuals. We show that, in our simulations, individuals that attend many meetings with many different people do not always have high betweenness values, even though they seem to be the ones that control the information flow. Singapore-MIT Alliance (SMA) 2005-12-14T19:42:37Z 2005-12-14T19:42:37Z 2006-01 Article http://hdl.handle.net/1721.1/30288 en Computer Science (CS) 2219317 bytes application/pdf application/pdf
spellingShingle social networks
universal hashing
privacy
information flow
pervasive computing
face-to-face meetings
models
location tracking
Rudolph, Larry
Zhenghao, Chen
Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy
title Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy
title_full Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy
title_fullStr Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy
title_full_unstemmed Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy
title_short Modeling Information Flow in Face-to-Face Meetings while Protecting Privacy
title_sort modeling information flow in face to face meetings while protecting privacy
topic social networks
universal hashing
privacy
information flow
pervasive computing
face-to-face meetings
models
location tracking
url http://hdl.handle.net/1721.1/30288
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