Abstract
Phenomenon of people with awareness of disseminating new information exists generally in social networks. In that case, people who have known the information would be likely to tell those whom haven’t known it. This progress could be regarded as the structure of networks coevolves with disseminating behavior. For investigating the interaction relationship between the structure and dynamics of growing networks, a model is proposed by depicting new information dissemination on the growing networks. At every step, a new node with several edges are added into the network by preferential rule proposed by BA model. By contrast, the range of preferential attachment of the new node is determined by the state of the old node which generating from the progress of information disseminating on the network. The analytical and numerical results show that the interaction between degree distribution and state of nodes becomes unobvious with time coevolving. Statistical property of propagation is affected by number of new edges adding at every step. Emerging of transition of density of nodes which have acquired the information implies that there always exists some nodes not knowing the information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)
Krapivsky, P.L., Redner, S.: Organization of growing random networks. Physical Review E 63, 66123 (2001)
Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)
Gross, T., Blasius, B.: Adaptive Coevolutionary Networks - A Review. JRS Interface (5), 259–271 (2008)
Cowan, R., Jonard, N.: Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control 28(8), 1557–1575 (2004)
Lambiotte, R., Panzarasa, P.: Communities, knowledge creation, and information diffusion. Journal of Informetrics 3(3), 180–190 (2009)
Gil, S., Zanette, D.H.: Coevolution of agents and networks: Opinion spreading and community disconnection. Phys. Lett. A 356, 89–95 (2006)
Keeling, M.J., Rand, D.A., Morris, A.J.: Correlation models for childhood epidemics. Proc. R. Soc. Lond. B 264, 1149–1156 (1997)
Holme, P., Newman, M.E.J.: Nonequilibrium phase transition in the coevolution of networks and opinions. Phys. Rev. E 74, 0561081 (2007)
Kozma, B., Barrat, A.: Consensus formation on adaptive networks. Phys. Rev. E 77, 0161021 (2008)
Kozma, B., Barrat, A.: Consensus formation on coevolving networks: groups’ formation and structure. J. Phys. A 41, 2240201 (2008)
Zhao, K., Juliette, S., Ginestra, B., Alain, B.: Social network dynamics of face-to-face interactions. Phys. Rev. E 83, 056109 (2011)
Gross, T., Dommar D’Lima, C., Blasius, B.: Epidemic dynamics on an adaptive network. Phys. Rev. Lett. 96, 208701 (2006)
Shaw, L.B., Schwartz, I.B.: Fluctuating epidemics on adaptive networks. Phys. Rev. E 77, 0661011 (2008)
Zanette, D.H.: Coevolution of agents and networks in an epidemiological model. arXiv:0707.1249 (2007)
Zanette, D.H., Gil, S.: Opinion spreading and agent segregation on evolving networks. Physica D 224, 156–165 (2006)
Vincent, M., Pierre-Andre, N., Laurent, H., Antoine, A., Louis, J.: Adaptive networks: Coevolution of disease and topology. Phys. Rev. E 82, 036116 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sui, Y., Shao, F., Sun, R., Li, S. (2012). Coevolving between Structure and Dynamics of Growing Networks. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-31362-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
eBook Packages: Computer ScienceComputer Science (R0)