Reconstructing an epidemic over time

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Date
2016-08-13
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Language
en
Pages
10
1835-1844
Series
KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Volume 13-17-August-2016
Abstract
We consider the problem of reconstructing an epidemic over time, or, more general, reconstructing the propagation of an activity in a network. Our input consists of a temporal network, which contains information about when two nodes interacted, and a sample of nodes that have been reported as infected. The goal is to recover the flow of the spread, including discovering the starting nodes, and identifying other likely-infected nodes that are not reported. The problem we consider has multiple applications, from public health to social media and viral marketing purposes. Previous work explicitly factor-in many unrealistic assumptions: it is assumed that (a) the underlying network does not change; (b) we have access to perfect noise-free data; or (c) we know the exact propagation model. In contrast, we avoid these simplifications: we take into account the temporal network, we require only a small sample of reported infections, and we do not make any restrictive assumptions about the propagation model. We develop CulT, a scalable and effective algorithm to reconstruct epidemics that is also suited for online settings. CulT works by formulating the problem as that of a temporal Steiner-tree computation, for which we design a fast algorithm leveraging the specific problem structure. We demonstrate the effcacy of the proposed approach through extensive experiments on diverse datasets.
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| openaire: EC/H2020/654024/EU//SoBigData
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Rozenshtein, P, Gionis, A, Prakash, B A & Vreeken, J 2016, Reconstructing an epidemic over time . in KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . vol. 13-17-August-2016, ACM, pp. 1835-1844, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, United States, 13/08/2016 . https://doi.org/10.1145/2939672.2939865