{"id":163654,"date":"2011-01-01T00:00:00","date_gmt":"2011-01-01T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/distance-constraint-reachability-computation-in-uncertain-graphs\/"},"modified":"2018-10-16T19:58:13","modified_gmt":"2018-10-17T02:58:13","slug":"distance-constraint-reachability-computation-in-uncertain-graphs","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/distance-constraint-reachability-computation-in-uncertain-graphs\/","title":{"rendered":"Distance-Constraint Reachability Computation in Uncertain Graphs"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Driven by the emerging network applications, querying and mining uncertain graphs has become increasingly important. In this paper, we investigate a fundamental problem concerning uncertain graphs, which we call the distance-constraint reachability (DCR) problem: Given two vertices s and t, what is the probability that the distance from s to t is less than or equal to a user-de\ufb01ned threshold d in the uncertain graph? Since this problem is #P-Complete, we focus on ef\ufb01ciently and accurately approximating DCR online. Our main results include two new estimators for the probabilistic reachability. One is a Horvitz Thomson type estimator based on the unequal probabilistic sampling scheme, and the other is a novel recursive sampling estimator, which effectively combines a deterministic recursive computational procedure with a sampling process to boost the estimation accuracy. Both estimators can produce much smaller variance than the direct sampling estimator, which considers each trial to be either 1 or 0. We also present methods to make these estimators more computationally ef\ufb01cient. The comprehensive experiment evaluation on both real and synthetic datasets demonstrates the ef\ufb01ciency and accuracy of our new estimators.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Driven by the emerging network applications, querying and mining uncertain graphs has become increasingly important. In this paper, we investigate a fundamental problem concerning uncertain graphs, which we call the distance-constraint reachability (DCR) problem: Given two vertices s and t, what is the probability that the distance from s to t is less than or [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Very Large Data Bases Endowment Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the VLDB Endowment, the 37th International Conference on Very Large Data Bases (VLDB 2011)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Proceedings of the VLDB Endowment, the 37th International Conference on Very Large Data Bases (VLDB 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