IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 3- Issue 2, February 2014 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Differential Model Of The Spatial Spread Of Information

[Full Text]

 

AUTHOR(S)

Agwu I. A, Abdulrahman S, Kalu A.U, Inyama S. C

 

KEYWORDS

Index Terms-spatial spread, diffusion, information wave, informed-uninformed class, oscillatory behavior, linearize stability

 

ABSTRACT

Abstract: Sociologists recognize phenomenon called social diffusion, which is the spreading of a piece of information, a technological innovation or a cultural fad among a population. This paper, proposes a differential model of the spread of information among a population whose size is known. The members of the population N(x,t) are grouped into two classes according to their information status: informed class I(x,t) and uninformed class U(x,t).We incorporated a diffusion term into the system as a tool for spreading information over significant distances. We investigate the spatial spread of an information into a uniform population of uninformed .Coupled conditions on threshold parameters which determine whether information will flow or not and condition for the existence of such travelling information wave and speed of propagation of information are determined. The system shows possibility of oscillatory behavior and stable dynamics

 

REFERENCES

[1]. E. Aghari, R. Burioni, D. Cassi. And F.M Neri ‘Efficiency of information spreading in a population of diffusing agents’ Physical Review E. vol.73, no.4, pp 46138, 2006.

[2]. [2] A. Apolloni, K.Channakes, D. Lisa, K. Maleg, K. Chris, L. Bryan, and S. Samarth ‘A study of information Diffusion over a realistic social network model’,2003.

[3]. J. Brown and Reinegen ‘Social ties and word–of–mouth referral behavior’ Journal of Consumer Research, Vol.14, no. 3, pp 350-362, 1987.

[4]. D .Crandall, J. Cosley, D. Huttenlocher, D. P.Kleinberg, J.M and S.Suri ‘feedback effects between similarity and social influence in online communities, in proceedings of the 14th CM SIGKDD international conference on knowledge Discovery and data mining’, pp160-168, 2008

[5]. P. Domingos ‘Mining social networks for viral marketing IEEE intelligent system’. Vol 20 no.1, pp 80-82, 2005.

[6]. S. R. Dunbar ‘Travelling wave solution of diffusive Lotka-Volterra equations: a Leteroclinic connection in R4, Trans.amer.math,soc. Vol.268, pp 557-594, 1984.

[7]. R.G. Frank, D.W.Maurice and P. F William ‘A First Course in Mathematical Modeling’ Thomson Books/Cole C.A, USA., 2003

[8]. D. Kempe, J.M Kleinberg and E. Tardos,’Maximizing the spread of influence through social network in proceeding of the 9th ACM SIGKDD international conference on knowledge Discovering and data mining’, pp 137-147, 2003.

[9]. Kempe D, Kleinberg J. M and Tardos E. ‘Influential nodes in a diffusion model for social networks’. In ICALP; pp 1127-1138, 2005..

[10]. J. D.Murrary Mathematical Biology, Springer-Verlag. New York’ 1990..

[11]. M. Nekovee, Y. Moreno, G. Bianconi and M. Marsili. ‘Theory of rumour spreading in Complex social network. Physical A; Statistical mechanics and its application vol.374 no.1, pp 457 – 470, 2007.

[12]. E.M Rogers,’Diffusion of innovation. New York free press of Glencoe’, 1962.

[13]. M. Richardson. and P. Domingos ‘Mining knowledge-sharing sites for viral marketing. In proceeding of the 8th ACM SIGKDD international conference on knowledge Discovery and data mining, pp 61 – 70, 2002.