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Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Transversing itemset lattices with statistical metric pruning
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Molecular feature mining in HIV data
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Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding the most interesting patterns in a database quickly by using sequential sampling
The Journal of Machine Learning Research
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Optimal assignment kernels for attributed molecular graphs
ICML '05 Proceedings of the 22nd international conference on Machine learning
GraphRank: Statistical Modeling and Mining of Significant Subgraphs in the Feature Space
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Fg-index: towards verification-free query processing on graph databases
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Discovering Significant Patterns
Machine Learning
ORIGAMI: Mining Representative Orthogonal Graph Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Tree2: decision trees for tree structured data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification of software behaviors for failure detection: a discriminative pattern mining approach
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Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying bug signatures using discriminative graph mining
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Graph classification based on pattern co-occurrence
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Frequent subgraph pattern mining on uncertain graph data
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L2 norm regularized feature kernel regression for graph data
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Output space sampling for graph patterns
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Mining graph patterns efficiently via randomized summaries
Proceedings of the VLDB Endowment
GConnect: a connectivity index for massive disk-resident graphs
Proceedings of the VLDB Endowment
Towards proximity pattern mining in large graphs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
GAIA: graph classification using evolutionary computation
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Authorship classification: a syntactic tree mining approach
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IEEE Computational Intelligence Magazine
Diagnosing memory leaks using graph mining on heap dumps
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Boosting with structure information in the functional space: an application to graph classification
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Semi-supervised feature selection for graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient algorithms based on relational queries to mine frequent graphs
PIKM '10 Proceedings of the 3rd workshop on Ph.D. students in information and knowledge management
DESSIN: mining dense subgraph patterns in a single graph
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
A statistical interestingness measures for XML based association rules
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Software-defect localisation by mining dataflow-enabled call graphs
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Latent structure pattern mining
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Mining closed discriminative dyadic sequential patterns
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Efficient discovery of frequent subgraph patterns in uncertain graph databases
Proceedings of the 14th International Conference on Extending Database Technology
Scalable graph analyzing approach for software fault-localization
Proceedings of the 6th International Workshop on Automation of Software Test
A log-linear approach to mining significant graph-relational patterns
Data & Knowledge Engineering
Classifying graphs using theoretical metrics: a study of feasibility
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Authorship classification: a discriminative syntactic tree mining approach
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Dual active feature and sample selection for graph classification
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GBASE: a scalable and general graph management system
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LGM: mining frequent subgraphs from linear graphs
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Use of schema associative mapping for synchronization of the virtual machine audit logs
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JACKSTRAWS: picking command and control connections from bot traffic
SEC'11 Proceedings of the 20th USENIX conference on Security
Software fault localization via mining execution graphs
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Mining significant time intervals for relationship detection
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Learning from graph data by putting graphs on the lattice
Expert Systems with Applications: An International Journal
Indexing and mining topological patterns for drug discovery
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Semi-supervised clustering of graph objects: a subgraph mining approach
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gbase: an efficient analysis platform for large graphs
The VLDB Journal — The International Journal on Very Large Data Bases
Graph classification: a diversified discriminative feature selection approach
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Efficient breadth-first search on large graphs with skewed degree distributions
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Mining discriminative subgraphs from global-state networks
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With ever-increasing amounts of graph data from disparate sources, there has been a strong need for exploiting significant graph patterns with user-specified objective functions. Most objective functions are not antimonotonic, which could fail all of frequency-centric graph mining algorithms. In this paper, we give the first comprehensive study on general mining method aiming to find most significant patterns directly. Our new mining framework, called LEAP (Descending Leap Mine), is developed to exploit the correlation between structural similarity and significance similarity in a way that the most significant pattern could be identified quickly by searching dissimilar graph patterns. Two novel concepts, structural leap search and frequency descending mining, are proposed to support leap search in graph pattern space. Our new mining method revealed that the widely adopted branch-and-bound search in data mining literature is indeed not the best, thus sketching a new picture on scalable graph pattern discovery. Empirical results show that LEAP achieves orders of magnitude speedup in comparison with the state-of-the-art method. Furthermore, graph classifiers built on mined patterns outperform the up-to-date graph kernel method in terms of efficiency and accuracy, demonstrating the high promise of such patterns.