Controlling Propagation at Group Scale on Networks
Abhijin Adiga, Anil Vullikanti
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.
- Date of publication:
- IEEE International Conference on Data Mining