Abstract
Communicable disease models have been studied using classical mathematical differential equations for a long time now. It is important to study the communicable disease models, so that one can come up with a good response system to contain the spread of viruses. Social networks are susceptible to the rapid spread of malicious information, commonly referred to as rumors. Rumors often spread rapidly through the network and, if not contained quickly, can be harmful. This chapter describes a method for identifying highly connected nodes in a social network and using these nodes to build immunity against such malicious information. To describe this method, this chapter draws inspiration from two well-established topics in the area of biology: one is the spread of communicable diseases in human population and second is how human body builds immunity against diseases as described in Chap. 5. In case of communicable diseases, it would be very simplistic if we only consider that an infected node can transmit its disease to its nearest neighbors. More realistically speaking, it is possible that an infected node can develop random links with other nodes in the system. The spread of communicable diseases is controlled by both these factors. An infected node with capability to have several random links is capable of spreading the disease through the network faster. We can postulate that certain nodes in a social network exhibit similar behavior and can be defined as highly connected nodes in the network. Once such nodes are identified, the concept of weighting functions is introduced that can be attached to messages passing through such nodes. This chapter describes how the spread of malicious information can be controlled by a community of such highly connected nodes, using the concept of weighted functions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Athuraliya, S., Low, S. H., Li, V. H., & Yin, Q. (2001). REM: active queue management. IEEE Network, 15(3), 48–53.
Bader, D. A., Kintali, S., Madduri, K., & Mihail, M. (2007). Approximating betweenness centrality. In Algorithms and models for the web-graph (pp. 124–137). Berlin: Springer.
Barabási, A. L. (2009). Scale-free networks: A decade and beyond. Science, 325(5939), 412.
Bojic, I., Lipic, T., & Podobnik, V. (2012). Bio-inspired clustering and data diffusion in machine social networks. In Computational social networks (pp. 51–79). London: Springer.
Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., & Shir, E. (2007). A model of Internet topology using k-shell decomposition. Proceedings of the National Academy of Sciences, 104(27), 11150–11154.
Chen, P. Y., & Chen, K. C. (2010). Information epidemics in complex networks with opportunistic links and dynamic topology In Global Telecommunications Conference (GLOBECOM 2010) (pp. 1–6).
Chen, D., Lü, L., Shang, M. S., Zhang, Y. C., & Zhou, T. (2012). Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications, 391(4), 1777–1787.
Choraś, M., Manso, M., Puchalski, D., Kozik, R., & Samp, K. (2013). Online social networks: Emerging security and safety applications. In Image Processing and Communications Challenges 4 (pp. 291–302). Berlin: Springer.
Dinh, T. N., Shen, Y., & Thai, M. T. (2012). The walls have ears: Optimize sharing for visibility and privacy in online social networks. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 1452–1461).
Doerr, B., Fouz, M., & Friedrich, T. (2012). Why rumors spread fast in social networks. In Magazine Communications of the ACM.
Gade, P. M., & Sinha, S. (2005). Dynamic transitions in small world networks: Approach to equilibrium limit. Physical Review E-Statistical, Nonlinear and Soft Matter Physics 72(5), 052903_1–052903_4.
Gao, C., Lan, X., Zhang, X., & Deng, Y. (2013). A bio-inspired methodology of identifying influential nodes in complex networks.
Guha, R., Kumar, R., Raghavan, P., & Tomkins, A. (2004). Propagation of trust and distrust. In Proceeding of International Conference on World Wide Web (Vol. 13, pp. 403–412).
Goffman, W., & Newill, V. A. (1964). Generalization of epidemic theory. Nature, 204(4955), 225–228.
Hethcote, H. W. (1976). Qualitative analyses of communicable disease models. Mathematical Biosciences, 28(3), 335–356.
Ilyas, M. U., & Radha, H. (2011). Identifying influential nodes in online social networks using principal component centrality. In 2011 IEEE International Conference on Communications (ICC) (pp. 1–5).
Jesan, T., Menon, G. I., & Sinha, S. (2010). Epidemiological dynamics of the 2009 influenza A (H1N1) v outbreak in India. arXiv preprint arXiv:1006.0685.
Kempe, D., Kleinberg, J., & Tardos, É. (2005). Influential nodes in a diffusion model for social networks. In Automata, languages and programming (pp. 1127–1138). Berlin: Springer.
Kephart, J. O., & White, S. R. (1991). Directed-graph epidemiological models of computer viruses. In 1991 IEEE Computer Society Symposium on Research in Security and Privacy, Proceedings (pp. 343–359).
Leskovec, J. (2011). Social media analytics: tracking, modeling and predicting the flow of information through networks. In Proceedings of the 20th International Conference Companion on World Wide Web (pp. 277–278). ACM.
Li, Q., Zhou, T., Lü, L., & Chen, D. (2014). Identifying influential spreaders by weighted LeaderRank. Physica A: Statistical Mechanics and its Applications, 404, 47–55.
Liaqat, H. B., Xia, F., Yang, Q., Xu, Z., Ahmed, A. M., & Rahim, A. (2014). Bio-inspired packet dropping for adhoc social networks. International Journal of Communication Systems.
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007). Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (pp. 29–42).
Okamoto, K., Chen, W., & Li, X. Y. (2008). Ranking of closeness centrality for large-scale social networks. In Frontiers in algorithmics (pp. 186–195). Berlin: Springer.
Pan, R. K., & Sinha, S. (2008). Modular networks with hierarchical organization: The dynamical implications of complex structure. Pramana, 71(2), 331–340.
Rathore, H., & Samant, A. (2012). A system for building immunity in social networks. In 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC) (pp. 20–24).
Rathore, H., Ranwa, S., & Samant, A. (2012), Modular network effects on communicable disease models. In 2012 Sixth Asia Modelling Symposium (AMS) (pp. 126–131).
Rivero, J., Cuadra, D., Calle, F. J., & Isasi, P. (2011). A bio-inspired algorithm for searching relationships in social networks. In 2011 International Conference on Computational Aspects of Social Networks (CASoN) (pp. 60–65). IEEE.
Song, Y., Karras, P., Nobari, S., Cheliotis, G., Xue, M., & Bressan, S. (2012). Discretionary social network data revelation with a user-centric utility guarantee. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 1572–1576).
Tsuchiya, T., & Kikuno, T. (2004). An adaptive mechanism for epidemic communication. In Biologically inspired approaches to advanced information technology. (pp. 306–316). Berlin: Springer.
Vahdat, A., & Becker, D. (2000). Epidemic routing for partially connected ad hoc networks (p. 18). Technical Report CS-200006, Duke University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Rathore, H. (2016). Information Epidemics and Social Networking. In: Mapping Biological Systems to Network Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29782-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-29782-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29780-4
Online ISBN: 978-3-319-29782-8
eBook Packages: EngineeringEngineering (R0)