Abstract
In large populations of autonomous individuals, the propagation of ideas, strategies or infections is determined by the composite effect of interactions between individuals. The propagation of concepts in a population is a form of influence spread and can be modelled as a cascade from a set of initial individuals through the population. Understanding influence spread and information cascades has many applications, from informing epidemic control and viral marketing strategies to understanding the emergence of conventions in multi-agent systems. Existing work on influence spread has mainly considered single concepts, or small numbers of blocking (exclusive) concepts. In this paper we focus on non-blocking cascades, and propose a new model for characterising concept interaction in an independent cascade. Furthermore, we propose two heuristics, Concept Aware Single Discount and Expected Infected, for identifying the individuals that will maximise the spread of a particular concept, and show that in the non-blocking multi-concept setting our heuristics out-perform existing methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 7–15 (2008)
Apt, K.R., Markakis, E.: Diffusion in social networks with competing products. In: Persiano, G. (ed.) SAGT 2011. LNCS, vol. 6982, pp. 212–223. Springer, Heidelberg (2011)
Borodin, A., Filmus, Y., Oren, J.: Threshold models for competitive influence in social networks. In: Internet and Network Economics, pp. 539–550 (2010)
Brummitt, C.D., Lee, K.-M., Goh, K.-I.: Multiplexity-facilitated cascades in networks. Phys. Rev. E 85(4), 45–102 (2012)
Chakrabarti, D., Wang, Y., Wang, C., Leskovec, J., Faloutsos, C.: Epidemic thresholds in real networks. ACM TISSEC 10(4), 1 (2008)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208 (2009)
Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: IEEE 10th International Conference on Data Mining, pp. 88–97 (2010)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)
Ferreira, S.C., Castellano, C., Pastor-Satorras, R.: Epidemic thresholds of the susceptible-infected-susceptible model on networks: a comparison of numerical and theoretical results. Phys. Rev. E 86(4), 41–125 (2012)
Goldenberg, J., Libai, B., Muller, E.: Using complex systems analysis to advance marketing theory development. Acad. Mark. Sci. Rev. 2001(9), 1–20 (2001)
Goyal, S., Kearns, M.: Competitive contagion in networks. In: Proceedings of the 44th Annual ACM Symposium on Theory of Computing, pp. 759–774 (2012)
He, X., Song, G., Chen, W., Jiang, Q.: Influence blocking maximization in social networks under the competitive linear threshold model. In: SIAM International Conference on Data Mining, pp. 463–474 (2012)
Karrer, B., Newman, M.: Competing epidemics on complex networks. Phys. Rev. E 84(3), 36–106 (2011)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2007)
Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86(14), 3200 (2001)
Pathak, N., Banerjee, A., Srivastava, J.: A generalized linear threshold model for multiple cascades. In: IEEE 10th International Conference on Data Mining, pp. 965–970 (2010)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70 (2002)
Sahneh, F.D., Scoglio, C.: May the best meme win!: new exploration of competitive epidemic spreading over arbitrary multi-layer networks (2013). arXiv:1308.4880
Sanz, J., Xia, C.-Y., Meloni, S., Moreno, Y.: Dynamics of interacting diseases (2014). arXiv:1402.4523
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Archbold, J., Griffiths, N. (2016). Maximising Influence in Non-blocking Cascades of Interacting Concepts. In: Gaudou, B., Sichman, J. (eds) Multi-Agent Based Simulation XVI. MABS 2015. Lecture Notes in Computer Science(), vol 9568. Springer, Cham. https://doi.org/10.1007/978-3-319-31447-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-31447-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31446-4
Online ISBN: 978-3-319-31447-1
eBook Packages: Computer ScienceComputer Science (R0)