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|Title:||Conditional reliability in uncertain graphs||Authors:||Khan, Arijit
DRNTU::Engineering::Computer science and engineering
|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.||URI:||https://hdl.handle.net/10356/103688
|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: https://doi.org/10.1109/TKDE.2018.2816653||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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