Abstract—Identifying rumor sources in
social networks plays a critical role in limiting the damage caused by them
through the timely quarantine of the sources. However, the temporal variation
in the topology of social networks and the ongoing dynamic processes challenge
our traditional source identification techniques that are considered in static
networks. In this paper, we borrow an idea from criminology and propose a novel
method to overcome the challenges. First, we reduce the time-varying networks
to a series of static networks by introducing a time-integrating window.
Second, instead of inspecting every individual in traditional techniques, we
adopt a reverse dissemination strategy to specify a set of suspects of the real
rumor source. This process addresses the scalability issue of source
identification problems, and therefore dramatically promotes the efficiency of
rumor source identification. Third, to determine the real source from the
suspects, we employ a novel microscopic rumor spreading model to calculate the
maximum likelihood (ML) for each suspect. The one who can provide the largest
ML estimate is considered as the real source. The evaluations are carried out
on real social networks with time-varying topology. The experiment results show
that our method can reduce 60% − 90% of the source seeking area in
various time-varying social networks. The results further indicate that our
method can accurately identify the real source, or an individual who is very
close to the real source. To the best of our knowledge, the proposed method is
the first that can be used to identify rumor sources in time-varying social
networks.
Conclusion
In this paper, we explore the problem of rumor source
identification in time-varying social networks that can be reduced to a series
of static networks by introducing a time-integrating window. In order to
address the challenges posted by time-varying social networks, we adopted two
innovative methods. First, we utilized a novel reverse dissemination method
which can sharply narrow down the Any Query Call Us:
9566355386
scale of suspicious
sources. This addresses the scalability issue in this research area and
therefore dramatically promotes the efficiency of rumor source identification.
Then, we introduced an analytical model for rumor spreading in time-varying
social networks. Based on this model, we calculated the maximum likelihood of
each suspect to determine the real source from the suspects.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
• System : Pentium IV
2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44
Mb.
• Monitor : 15 VGA
Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
• Operating system : -
Windows XP/7.
• Coding Language :
JAVA/J2EE
• Data Base : MYSQL
REFERENCES
[1] F. Peter. (2013, April 23) ‗bogus‘ ap tweet about
explosion at the white house wipes billions off us markets. The Telegraph,
Finance/Market. Washington.
[2] B. Ribeiro, N. Perra, and A. Baronchelli,
―Quantifying the effect of temporal resolution on time-varying
networks,‖ Sci- entific reports, vol. 3, 2013. Any
Query Call Us: 9566355386
[3] M. P. Viana, D. R. Amancio, and L. d. F. Costa,
―On timevarying collaboration networks,‖ Journal of Informetrics,
vol. 7, no. 2, pp. 371–378, 2013.