An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
ICDM '01 Proceedings of the 2001 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
SPIN: mining maximal frequent subgraphs from graph databases
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
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Identifying bug signatures using discriminative graph mining
Proceedings of the eighteenth international symposium on Software testing and analysis
Graph classification based on pattern co-occurrence
Proceedings of the 18th ACM conference on Information and knowledge management
Classifying graphs using theoretical metrics: a study of feasibility
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Dual active feature and sample selection for graph classification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Indexing and mining topological patterns for drug discovery
Proceedings of the 15th International Conference on Extending Database Technology
Semi-supervised clustering of graph objects: a subgraph mining approach
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Graph classification: a diversified discriminative feature selection approach
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient breadth-first search on large graphs with skewed degree distributions
Proceedings of the 16th International Conference on Extending Database Technology
Mining discriminative subgraphs from global-state networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Discriminative subgraphs are widely used to define the feature space for graph classification in large graph databases. Several scalable approaches have been proposed to mine discriminative subgraphs. However, their intensive computation needs prevent them from mining large databases. We propose an efficient method GAIA for mining discriminative subgraphs for graph classification in large databases. Our method employs a novel subgraph encoding approach to support an arbitrary subgraph pattern exploration order and explores the subgraph pattern space in a process resembling biological evolution. In this manner, GAIA is able to find discriminative subgraph patterns much faster than other algorithms. Additionally, we take advantage of parallel computing to further improve the quality of resulting patterns. In the end, we employ sequential coverage to generate association rules as graph classifiers using patterns mined by GAIA. Extensive experiments have been performed to analyze the performance of GAIA and to compare it with two other state-of-the-art approaches. GAIA outperforms the other approaches both in terms of classification accuracy and runtime efficiency.