The Role of Mobile Social Networks in Information Diffusion
Abstract
Mobile Social Networks have become ubiquitous in our daily
life; as such it has attracted great research interests recently.
Online social networking technologies enable individuals
tosimultaneously share information with any number of peers.
Quantifying the causal effect of these mediums on the
dissemination of information requires not only identification
of who influences whom, but also of whether individuals
would still propagate information in the absence of social
signals about that information.Before fully utilizing mobile
social networks as a platform for viral marketing, many
challenges have to be addressed. In this paper, we address the
problem of identifying a small number of individuals through
whom the information can be diffused to the network as soon
as possible, referred to as the diffusion minimization problem.
The real trace compared to existing algorithms, and the
distributed set-cover algorithm outperforms the approximation
algorithm in the real trace in terms of diffusion time.
Full Text:
PDFReferences
Zongqing Lu, Yonggang Wen, Weizhan Zhang, Qinghua
Zheng, and Guohong Cao,"Towards Information Diffusion in
Mobile Social Networks", IEEE Transactions on Mobile
Computing Vol.PP, Issue 99, July 2015
W. Chen, C. Wang, and Y.Wang, “Scalable influence
maximization for prevalent viral marketing in largescalesocial
networks,” in Proc. of ACM SIGKDD, 2010.
T. Ning, Z. Yang, H. Wu, and Z. Han, “Self-interest-drive
incentives for ad dissemination in autonomous mobilesocial
networks,” in Proc. of IEEE INFOCOM, 2013.
K. K. Rachuri, C. Efstratiou, I. Leontiadis, C. Mascolo,
andP. J. Rentfrow, “Metis: Exploring mobile phone
sensingoffloading for efficiently supporting social
sensingapplications,” in Proc. of IEEE PerCom, 2013.
L. McNamara, C. Mascolo, and L. Capra, “Media
sharingbased on colocation prediction in urban transport,” in
Proc.of ACM MobiCom, 2008.
W. Hu, G. Cao, S. V. Krishanamurthy, and P.
Mohapatra,“Mobility-assisted energy-aware user contact
detection in mobile social networks,” in Proc. of IEEE
ICDCS, 2013.
Z. Lu, X. Sun, Y. Wen, and G. Cao, “Skeleton
constructionin mobile social networks: Algorithm and
application,” inProc. Of IEEE SECON, 2014.
Z. Lu, Y. Wen, and G. Cao, “Information diffusion in
mobile social networks: The speed perspective,” in Proc.
ofIEEE INFOCOM, 2014.
H. Ma, H. Yang, M. R. Lyu, and I. King, “Mining
socialnetworks using heat diffusion processes for
marketingcandidates selection,” in Proc. of ACM CIKM,
W. Chen,W. Lu, and N. Zhang, “Time-critical
influencemaximization in social networks with time-delayed
diffusion process,” in Proc. of AAAI, 2012.
H. Zhang, T. N. Dinh, and M. T. Thai, “Maximizing
thespread of positive influence in online social networks,”
inProc. of IEEE ICDCS, 2013.
B. Han, J. Li, and A. Srinivasan, “Your friends have
morefriends than you do: Identifying influential mobile users
through random-walk sampling,” Networking,
IEEE/ACMTransactions on, vol. 22, no. 5, pp. 1389–1400,
Z. Lu, Y. Wen, and G. Cao, “Community detection
inweighted networks: Algorithms and applications,” in Proc.of
IEEE PerCom, 2013.
X. Zhang and G. Cao, “Transient community detection
and its application to data forwarding in delay
tolerantnetworks,” in Proc. of IEEE ICNP, 2013.
A.Lancichinetti and S. Fortunato, “Benchmarks for
testing community detection algorithms on directed and
weighted graphs with overlapping communities,” Physical
Review E,vol. 80, no. 1, p. 016118, 2009.
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