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GAIA: Graph Classification Using Evolutionary Computation
| Citations: | 12 - 1 self |
BibTeX
@MISC{Jin_gaia:graph,
author = {Ning Jin and Calvin Young and Wei Wang},
title = {GAIA: Graph Classification Using Evolutionary Computation},
year = {}
}
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Abstract
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-ofthe-art approaches. GAIA outperforms the other approaches both in terms of classification accuracy and runtime efficiency.
Keyphrases
discriminative subgraphs graph classification using evolutionary computation large database graph classification state-ofthe-art approach arbitrary subgraph pattern exploration order discriminative subgraph pattern novel subgraph extensive experiment classification accuracy feature space intensive computation need runtime efficiency sequential coverage efficient method gaia subgraph pattern space biological evolution graph classifier large graph database several scalable approach association rule
