ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Computing Frequent Graph Patterns from Semistructured Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SPIN: mining maximal frequent subgraphs from graph databases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Graph indexing based on discriminative frequent structure analysis
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2004
Summarizing itemset patterns using probabilistic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
MARGIN: Maximal Frequent Subgraph Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Subgraph Support in a Single Large Graph
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Effective and efficient itemset pattern summarization: regression-based approaches
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ORIGAMI: Mining Representative Orthogonal Graph Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
On effective presentation of graph patterns: a structural representative approach
Proceedings of the 17th ACM conference on Information and knowledge management
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
RING: An Integrated Method for Frequent Representative Subgraph Mining
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Output space sampling for graph patterns
Proceedings of the VLDB Endowment
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
DESSIN: mining dense subgraph patterns in a single graph
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
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Frequent subgraph mining has been an important research problem in the literature. However, the huge number of discovered frequent subgraphs becomes the bottleneck for exploring and understanding the generated patterns. In this paper, we propose to summarize frequent subgraphs with an independence probabilistic model, with the goal to restore the frequent subgraphs and their frequencies accurately from a compact summarization model. To achieve a good summarization quality, our summarization framework allows users to specify an error tolerance σ, and our algorithms will discover k summarization templates in a top-down fashion and keep the frequency restoration error within σ. Experiments on real graph datasets show that our summarization framework can effectively control the frequency restoration error within 10% with a concise summarization model.