Robust Cascade Reconstruction by Steiner Tree Sampling

No Thumbnail Available
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
This publication is imported from Aalto University research portal.
View publication in the Research portal

Other link related to publication
Date
2018-11
Major/Subject
Mcode
Degree programme
Language
en
Pages
637-646
Series
2018 IEEE International Conference on Data Mining (ICDM), IEEE International Conference on Data Mining Proceedings
Abstract
We consider a network where an infection has taken place and a subset of infected nodes has been partially observed. Our goal is to reconstruct the underlying cascade that is likely to have generated these observations. We reduce this cascadereconstruction problem to computing the marginal probability that a node is infected given the partial observations, which is a #P-hard problem. To circumvent this issue, we resort to estimating infection probabilities by generating a sample of probable cascades, which span the nodes that have already been observed to be infected, and avoid the nodes that have been observed to be uninfected. The sampling problem corresponds to sampling directed Steiner trees with a given set of terminals, which is a problem of independent interest and has received limited attention in the literature. For the latter problem we propose two novel algorithms with provable guarantees on the sampling distribution of the returned Steiner trees. The resulting method improves over state-of-the-art approaches that often make explicit assumptions about the infection-propagation model, or require additional parameters. Our method provides a more robust approach to the cascadereconstruction problem, which makes weaker assumptions about the infection model, requires fewer additional parameters, and can be used to estimate node infection probabilities. We experimentally validate the proposed reconstruction algorithm on realworld graphs with both synthetic and real cascades. We show that our method outperforms all other baseline strategies in most cases.
Description
| openaire: EC/H2020/654024/EU//SoBigData
Keywords
Other note
Citation
Xiao , H , Aslay , C & Gionis , A 2018 , Robust Cascade Reconstruction by Steiner Tree Sampling . in 2018 IEEE International Conference on Data Mining (ICDM) . IEEE International Conference on Data Mining Proceedings , IEEE , pp. 637-646 , IEEE International Conference on Data Mining , Singapore , Singapore , 17/11/2018 . https://doi.org/10.1109/ICDM.2018.00079