DocumentCode :
65767
Title :
Conjoining Speeds up Information Diffusion in Overlaying Social-Physical Networks
Author :
Yagan, Osman ; Dajun Qian ; Junshan Zhang ; Cochran, Douglas
Author_Institution :
CyLab, Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume :
31
Issue :
6
fYear :
2013
fDate :
Jun-13
Firstpage :
1038
Lastpage :
1048
Abstract :
We study the diffusion of information in an overlaying social-physical network. Specifically, we consider the following set-up: There is a physical information network where information spreads amongst people through conventional communication media (e.g., face-to-face communication, phone calls), and conjoint to this physical network, there are online social networks where information spreads via web sites such as Facebook, Twitter, FriendFeed, YouTube, etc. We quantify the size and the critical threshold of information epidemics in this conjoint social-physical network by assuming that information diffuses according to the SIR epidemic model. One interesting finding is that even if there is no percolation in the individual networks, percolation (i.e., information epidemics) can take place in the conjoint social-physical network. We also show, both analytically and experimentally, that the fraction of individuals who receive an item of information (started from an arbitrary node) is significantly larger in the conjoint social-physical network case, as compared to the case where the networks are disjoint. These findings reveal that conjoining the physical network with online social networks can have a dramatic impact on the speed and scale of information diffusion.
Keywords :
information dissemination; social networking (online); Facebook; FriendFeed; SIR epidemic model; Twitter; Web site; YouTube; communication media; conjoint social-physical network; face-to-face communication; information diffusion; information epidemics; information spread; online social network; overlaying social-physical network; phone call; physical information network; Coupled Social Networks; Information Diffusion; Percolation Theory; Random Graphs;
fLanguage :
English
Journal_Title :
Selected Areas in Communications, IEEE Journal on
Publisher :
ieee
ISSN :
0733-8716
Type :
jour
DOI :
10.1109/JSAC.2013.130606
Filename :
6517108
Link To Document :
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