Network reliability evaluation
Network performance modeling and simulation
Freenet: a distributed anonymous information storage and retrieval system
International workshop on Designing privacy enhancing technologies: design issues in anonymity and unobservability
The Combinatorics of Network Reliability
The Combinatorics of Network Reliability
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Building Low-Diameter P2P Networks
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
A New Monte-Carlo Method for Estimating the Failure Probability of an
A New Monte-Carlo Method for Estimating the Failure Probability of an
Do social networks improve e-commerce?: a study on social marketplaces
Proceedings of the first workshop on Online social networks
Combinatorial and computational properties of a diameter constrained network reliability model
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Managing and Mining Uncertain Data
Managing and Mining Uncertain Data
3-HOP: a high-compression indexing scheme for reachability query
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Probabilistic path queries in road networks: traffic uncertainty aware path selection
Proceedings of the 13th International Conference on Extending Database Technology
Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
GRAIL: scalable reachability index for large graphs
Proceedings of the VLDB Endowment
k-nearest neighbors in uncertain graphs
Proceedings of the VLDB Endowment
Efficiently answering probability threshold-based shortest path queries over uncertain graphs
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Querying uncertain data with aggregate constraints
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Discovering highly reliable subgraphs in uncertain graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient subgraph similarity search on large probabilistic graph databases
Proceedings of the VLDB Endowment
K-reach: who is in your small world
Proceedings of the VLDB Endowment
Injecting uncertainty in graphs for identity obfuscation
Proceedings of the VLDB Endowment
Efficient breadth-first search on large graphs with skewed degree distributions
Proceedings of the 16th International Conference on Extending Database Technology
Learning in probabilistic graphs exploiting language-constrained patterns
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Computing weight constraint reachability in large networks
The VLDB Journal — The International Journal on Very Large Data Bases
Simple, fast, and scalable reachability oracle
Proceedings of the VLDB Endowment
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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-defined threshold d in the uncertain graph? Since this problem is #P-Complete, we focus on efficiently 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 efficient. The comprehensive experiment evaluation on both real and synthetic datasets demonstrates the efficiency and accuracy of our new estimators.