Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
SIAM Journal on Computing
The Combinatorics of Network Reliability
The Combinatorics of Network Reliability
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On the K-simple shortest paths problem in weighted directed graphs
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Finding the k shortest simple paths: A new algorithm and its implementation
ACM Transactions on Algorithms (TALG)
Compressing probabilistic Prolog programs
Machine Learning
The Most Reliable Subgraph Problem
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Finding Reliable Subgraphs from Large Probabilistic Graphs
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
A lower bound on the sample size needed to perform a significant frequent pattern mining task
Pattern Recognition Letters
Frequent subgraph pattern mining on uncertain graph data
Proceedings of the 18th ACM conference on Information and knowledge management
Frequent subgraph mining on a single large graph using sampling techniques
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient discovery of frequent subgraph patterns in uncertain graph databases
Proceedings of the 14th International Conference on Extending Database Technology
Compression of weighted graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering highly reliable subgraphs in uncertain graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
GaMuSo: graph base music recommendation in a social bookmarking service
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Fast discovery of reliable k-terminal subgraphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A framework for path-oriented network simplification
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Reconstructing unsound data provenance view in scientific workflow
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Patterns and logic for reasoning with networks
Bisociative Knowledge Discovery
Review of bisonet abstraction techniques
Bisociative Knowledge Discovery
Simplification of networks by edge pruning
Bisociative Knowledge Discovery
Network compression by node and edge mergers
Bisociative Knowledge Discovery
Finding representative nodes in probabilistic graphs
Bisociative Knowledge Discovery
Mining frequent subgraphs over uncertain graph databases under probabilistic semantics
The VLDB Journal — The International Journal on Very Large Data Bases
Subgraph Extraction for Trust Inference in Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K. The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.