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Title: Conditional reliability in uncertain graphs
Authors: Khan, Arijit
Bonchi, Francesco
Gullo, Francesco
Nufer, Andreas
Keywords: Reliability
DRNTU::Engineering::Computer science and engineering
Uncertain Graphs
Issue Date: 2018
Source: Khan, A., Bonchi, F., Gullo, F., & Nufer, A. Conditional reliability in uncertain graphs. IEEE Transactions on Knowledge and Data Engineering, 30(11), 2078-2092. doi:10.1109/TKDE.2018.2816653
Series/Report no.: IEEE Transactions on Knowledge and Data Engineering
Abstract: Network reliability is a well-studied problem that requires to measure the probability that a target node is reachable from a source node in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Many approaches and problem variants have been considered in the literature, with the majority of them assuming that edge-existence probabilities are fixed. Nevertheless, in real-world graphs, edge probabilities typically depend on external conditions. In metabolic networks, a protein can be converted into another protein with some probability depending on the presence of certain enzymes. In social influence networks, the probability that a tweet of some user will be re-tweeted by her followers depends on whether the tweet contains specific hashtags. In transportation networks, the probability that a network segment will work properly or not, might depend on external conditions such as weather or time of the day. In this paper, we overcome this limitation and focus on conditional reliability , that is, assessing reliability when edge-existence probabilities depend on a set of conditions. In particular, we study the problem of determining the top- k conditions that maximize the reliability between two nodes. We deeply characterize our problem and show that, even employing polynomial-time reliability-estimation methods, it is NP -hard, does not admit any PTAS , and the underlying objective function is non-submodular. We then devise a practical method that targets both accuracy and efficiency. We also study natural generalizations of the problem with multiple source and target nodes. An extensive empirical evaluation on several large, real-life graphs demonstrates effectiveness and scalability of our methods.
ISSN: 1041-4347
DOI: 10.1109/TKDE.2018.2816653
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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