Computing edge-connectivity in multigraphs and capacitated graphs
SIAM Journal on Discrete Mathematics
A faster algorithm for finding the minimum cut in a graph
SODA '92 Proceedings of the third annual ACM-SIAM symposium on Discrete algorithms
Minimum cuts in near-linear time
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Journal of the ACM (JACM)
Graph theory and its applications
Graph theory and its applications
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A clustering algorithm based on graph connectivity
Information Processing Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 17th International Conference on Data Engineering
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Mining closed relational graphs with connectivity constraints
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering large dense subgraphs in massive graphs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Determining edge connectivity in 0(nm)
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
gPrune: a constraint pushing framework for graph pattern mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
SkyGraph: An Algorithm for Important Subgraph Discovery in Relational Graphs
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
MING: mining informative entity relationship subgraphs
Proceedings of the 18th ACM conference on Information and knowledge management
Finding maximal k-edge-connected subgraphs from a large graph
Proceedings of the 15th International Conference on Extending Database Technology
SkyDiver: a framework for skyline diversification
Proceedings of the 16th International Conference on Extending Database Technology
Efficiently computing k-edge connected components via graph decomposition
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A multiobjective evolutionary programming framework for graph-based data mining
Information Sciences: an International Journal
Three-objective subgraph mining using multiobjective evolutionary programming
Journal of Computer and System Sciences
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A significant number of applications require effective and efficient manipulation of relational graphs, towards discovering important patterns. Some example applications are: (i) analysis of microarray data in bioinformatics, (ii) pattern discovery in a large graph representing a social network, (iii) analysis of transportation networks, (iv) community discovery in Web data. The basic approach followed by existing methods is to apply mining techniques on graph data to discover important patterns, such as subgraphs that are likely to be useful. However, in some cases the number of mined patterns is large, posing difficulties in selecting the most important ones. For example, applying frequent subgraph mining on a set of graphs the system returns all connected subgraphs whose frequency is above a specified (usually user-defined) threshold. The number of discovered patterns may be large, and this number depends on the data characteristics and the frequency threshold specified. It would be more convenient for the user if "goodness" criteria could be set to evaluate the usefulness of these patterns, and if the user could provide preferences to the system regarding the characteristics of the discovered patterns. In this paper, we propose a methodology to support such preferences by applying subgraph discovery in relational graphs towards retrieving important connected subgraphs. The importance of a subgraph is determined by: (i) the order of the subgraph (the number of vertices) and (ii) the subgraph edge connectivity. The performance of the proposed technique is evaluated by using real-life as well as synthetically generated data sets.