Abstract
In this paper, we study practical strategies for controlling the behaviour of a synthetic social network modelling the dynamic diffusion of knowledge. The problem of controlling the evolution of complex networks has been extensively studied in recent years and remarkable theoretical results have been achieved. However, still largely unexplored is the analysis of realistic control strategies for complex networks and the special case of social networks. Our model of knowledge diffusion in a social network is used for simulating and evaluating possible control strategies of social network behaviour. Our approach is to exploit the controlled injection of random topics into some driver nodes for influencing the overall dynamics. This way, it is possible to modify some key control parameters in a deterministic way with realistic inputs, considering the strong practical constraints of social networks with respect to control measures. Control parameters considered are: The injection interval of random topics, the rate of driver nodes with respect to the network size, and the selection criteria of driver nodes. Finally, we discuss possible applications and the challenges that social networks pose to the issue of network control.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using rankingbased techniques. IEEE Transactions on Knowledge and Data Engineering 24(5), 896–911 (2012)
Allodi, L., Chiodi, L., Cremonini, M.: Self-organizing techniques for knowledge diffusion in dynamic social networks. In: Proceedings of Complex Networks Conference 2014 (CompleNet14). Bologna, Italy (2014)
Bradley, K., Smyth, B.: Improving recommendation diversity. In: Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland, pp. 85–94. Citeseer (2001)
Centola, D., Gonzalez-Avella, J.C., Eguiluz, V.M., San Miguel, M.: Homophily, cultural drift, and the co-evolution of cultural groups. Journal of Conflict Resolution 51(6), 905–929 (2007)
Cornelius, S.P., Kath, W.L., Motter, A.E.: Realistic control of network dynamics. Nature communications 4 (2013)
Cowan, N.J., Chastain, E.J., Vilhena, D.A., Freudenberg, J.S., Bergstrom, C.T.: Nodal dynamics, not degree distributions, determine the structural controllability of complex networks. PloS one 7(6), e38,398 (2012)
Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 160–168. ACM (2008)
Cremonini, M.: Introducing serendipity in a social network model of knowledge diffusion. Chaos, Solitons & Fractals 90, 64–71 (2016)
Easley, D., Kleinberg, J., et al.: Networks, crowds, and markets: Reasoning about a highly connected world. Significance 9, 43–44 (2012)
Gao, J., Liu, Y.Y., D’Souza, R.M., Barabási, A.L.: Target control of complex networks. Nature communications 5 (2014)
Goldstone, R.L., Gureckis, T.M.: Collective behavior. Topics in Cognitive Science 1(3), 412–438 (2009)
Golub, B., Jackson, M.O.: How homophily affects the speed of learning and best response dynamics (2012)
Kleinsman, J., Buckley, S.: Facebook study: a little bit unethical but worth it? Journal of Bioethical inquiry 12(2), 179–182 (2015)
Lin, C.T.: Structural controllability. IEEE Transactions on Automatic Control 19(3), 201–208 (1974)
Liu, Y.Y., Barabási, A.L.: Control principles of complex networks. arXiv preprint arXiv:1508.05384 (2015)
Liu, Y.Y., Slotine, J.J., Barabási, A.L.: Controllability of complex networks. Nature 473(7346), 167–173 (2011)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual review of sociology pp. 415–444 (2001)
Menichetti, G., Dall’Asta, L., Bianconi, G.: Network controllability is determined by the density of low in-degree and out-degree nodes. Physical review letters 113(7), 078,701 (2014)
Motter, A.E.: Networkcontrology. Chaos: An Interdisciplinary Journal of Nonlinear Science 25(9), 097,621 (2015)
Newman, M.: Networks: an introduction. Oxford university press (2010)
Newman, M.W., Sedivy, J.Z., Neuwirth, C.M., Edwards, W.K., Hong, J.I., Izadi, S., Marcelo, K., Smith, T.F.: Designing for serendipity: supporting end-user configuration of ubiquitous computing environments. In: Proceedings of the 4th conference on Designing interactive systems: processes, practices, methods, and techniques, pp. 147–156. ACM (2002)
Pariser, E.: The filter bubble: What the Internet is hiding from you. Penguin UK (2011)
Tucker, C.E.: Social networks, personalized advertising, and privacy controls. Journal of Marketing Research 51(5), 546–562 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Casamassima, F., Cremonini, M. (2017). Use of Random Topics as Practical Control Signals in a Social Network Model. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-50901-3_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50900-6
Online ISBN: 978-3-319-50901-3
eBook Packages: EngineeringEngineering (R0)