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SIGMOD
2010
ACM

GAIA: graph classification using evolutionary computation

9 years 9 months ago
GAIA: graph classification using evolutionary computation
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 perf...
Ning Jin, Calvin Young, Wei Wang
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where SIGMOD
Authors Ning Jin, Calvin Young, Wei Wang
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