Given an noisy or sampled snapshot of a network, like a contact-network or the blogosphere, in which an infection (or meme/virus) has been spreading for some time, what are the best nodes to immunize (vaccinate)? Manipulating graphs via node removal by itself is an important problem in multiple different domains like epidemiology, public health and social media. Moreover, it is important to account for uncertainty as typically surveillance data on who is infected is limited or the data is sampled. Efficient algorithms for such a problem can help public-health experts take more informed decisions.

In this paper, we study the problem of designing vaccine-distribution algorithms under an uncertain environment, with known information consisting of confirmed cases as well as a probability distribution of unknown cases. We formulate the NP-Hard Uncertain Data-Aware Vaccination problem, and design multiple efficient algorithms for factorizable distributions (including a novel sub-quadratic algorithm) which naturally take into account the uncertainty, while providing robust solutions. Finally, we show the effectiveness and scalability of our methods via extensive experiments on real datasets, including large epidemiological and social networks.

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Publication Details

Date of publication:
September 16, 2015
ACM International Conference on Information Managment
Association for Computing Machinery (ACM)