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2015 IEEE International Conference on Data Mining (ICDM) (2015)
Atlantic City, NJ, USA
Nov. 14, 2015 to Nov. 17, 2015
ISSN: 1550-4786
ISBN: 978-1-4673-9503-8
pp: 619-628
ABSTRACT
Given a network with groups, such as a contact-network grouped by ages, which are the best groups to immunize to control the epidemic? Equivalently, how to best choose communities in social networks like Facebook to stop rumors from spreading? Immunization is an important problem in multiple different domains like epidemiology, public health, cyber security and social media. Additionally, clearly immunization at group scale (like schools and communities) is more realistic due to constraints in implementations and compliance (e.g., it is hard to ensure specific individuals take the adequate vaccine). Hence efficient algorithms for such a "group-based" problem can help public-health experts take more practical decisions. However most prior work has looked into individual-scale immunization. In this paper, we study the problem of controlling propagation at group scale. We formulate novel so-called Group Immunization problems for multiple natural settings (for both threshold and cascade-based contagion models under both node-level and edge-level interventions) and develop multiple efficient algorithms, including provably approximate solutions. Finally, we show the effectiveness of our methods via extensive experiments on real and synthetic datasets.
INDEX TERMS
Vaccines, Resource management, Integrated circuit modeling, Immune system, Diffusion processes, Facebook, Public healthcare
CITATION

Y. Zhang, A. Adiga, A. Vullikanti and B. A. Prakash, "Controlling Propagation at Group Scale on Networks," 2015 IEEE International Conference on Data Mining (ICDM), Atlantic City, NJ, USA, 2015, pp. 619-628.
doi:10.1109/ICDM.2015.59
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