Issue No. 12 - Dec. (2016 vol. 28)
Yao Zhang , Department of Computer Science, Virginia Tech, Blacksburg, VA
Abhijin Adiga , NDSSL, Biocomplexity Institute of Virginia Tech, Blacksburg, VA
Sudip Saha , Department of Computer Science, Virginia Tech, Blacksburg, VA
Anil Vullikanti , Department of Computer Science, Virginia Tech, Blacksburg, VA
B. Aditya Prakash , Department of Computer Science, Virginia Tech, Blacksburg, VA
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 choose best communities in social media 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 a set of novel 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.
Immune system, Vaccines, Resource management, Data models, Media, Mathematical model, Public healthcare
Y. Zhang, A. Adiga, S. Saha, A. Vullikanti and B. A. Prakash, "Near-Optimal Algorithms for Controlling Propagation at Group Scale on Networks," in IEEE Transactions on Knowledge & Data Engineering, vol. 28, no. 12, pp. 3339-3352, 2016.