Abstract
Modelling the diffusion of information is one of the key areas related to activity within social networks. In this field, there is recent research associated with the use of community detection algorithms and the analysis of how the structure of communities is affecting the spread of information. The purpose of this article is to examine the mechanisms of diffusion of viral content with particular emphasis on cross community diffusion.
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References
Bampo, M., et al.: The Effects of the Social Structure of Digital Networks on Viral Marketing Performance. Information Systems Research 19(3), 273–290 (2008)
Barbieri, N., Bonchi, F., Manco, G.: Cascade-based Community Detection. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM 2013). ACM, Rome (2013)
Belák, V., Lam, S., Hayes, C.: Cross-Community Influence in Discussion Fora. In: ICWSM (2012)
Belák, V., Lam, S., Hayes, C.: Towards Maximising Cross-Community Information Diffusion. In: Proceedings of ASONAM 2012, pp. 171–178 (2012)
Blondel, V.D., et al.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 10, P10008 (2008)
Bródka, P., Kazienko, P., Musiał, K., Skibicki, K.: Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks. International Journal of Computational Intelligence Systems 5(3), 582–596 (2012)
Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press (2010)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3-5), 75–174 (2010)
Goyal, A., et al.: On Minimizing Budget and Time in Influence Propagation over Social Networks. In: Social Network Analysis and Mining. Springer (2012)
Jankowski, J., Michalski, R., Kazienko, P.: The Multidimensional Study of Viral Campaigns as Branching Processes. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 462–474. Springer, Heidelberg (2012)
Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)
Ma, H., Yang, H., Lyu, M.R., King, I.: Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM Conf. on Information and Knowledge Management, pp. 233–242 (2008)
Najar, A., Denoyer, L., Gallinari, P.: Predicting information diffusion on social networks with partial knowledge. In: WWW, pp. 1197–1204 (2012)
Wang, Y., et al.: Community-based greedy algorithm for mining top-K influential nodes in mobile social networks. In: ACM SIGKDD 2010, pp. 1039–1048. ACM, New York (2010)
Watts, D., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
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Jankowski, J., Kozielski, M., Filipowski, W., Michalski, R. (2013). The Diffusion of Viral Content in Multi-layered Social Networks. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_4
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DOI: https://doi.org/10.1007/978-3-642-40495-5_4
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