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2015 IEEE International Conference on Data Mining (ICDM) (2015)
Atlantic City, NJ, USA
Nov. 14, 2015 to Nov. 17, 2015
ISSN: 1550-4786
ISBN: 978-1-4673-9503-8
pp: 659-668
Uncertain graphs have been widely used to represent graph data with inherent uncertainty in structures. Reliability search is a fundamental problem in uncertain graph analytics. This paper studies a new problem, the top-k reliability search problem on uncertain graphs, that is, finding k vertices v with the highest reliabilities of connections from a source vertex s to v. Note that the existing algorithm for the threshold-based reliability search problem is inefficient for the top-k reliability search problem. We propose a new algorithm to efficiently solve the top-k reliability search problem. The algorithm adopts two important techniques, namely the BFS sharing technique and the offline sampling technique. The BFS sharing technique exploits overlaps among different sampled possible worlds of the input uncertain graph and performs a single BFS on all possible worlds simultaneously. The offline sampling technique samples possible worlds offline and stored them using a compact structure. The algorithm also takes advantages of bit vectors and bitwise operations to improve efficiency. Moreover, we generalize the top-k reliability search problem to the multi-source case and show that the multi-source case of the problem can be equivalently converted to the single-source case of the problem. Extensive experiments carried out on both real and synthetic datasets verify that the optimized algorithm outperforms the baselines by 1 -- 2 orders of magnitude in execution time while achieving comparable accuracy. Meanwhile, the optimized algorithm exhibits linear scalability with respect to the size of the input uncertain graph.
Reliability, Search problems, Approximation algorithms, Proteins, Approximation methods, Uncertainty, Monte Carlo methods

R. Zhu, Z. Zou and J. Li, "Top-k Reliability Search on Uncertain Graphs," 2015 IEEE International Conference on Data Mining (ICDM), Atlantic City, NJ, USA, 2015, pp. 659-668.
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