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Discriminative frequent subgraph mining with optimality guarantees

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Borgwardt,  KM
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.-P., Smola, A., et al. (2010). Discriminative frequent subgraph mining with optimality guarantees. Statistical Analysis and Data Mining, 3(5), 302-318. doi:10.1002/sam.10084.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BDBA-9
Abstract
The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK, that combines two central advantages. First, it optimizes a submodular quality criterion, which means that we can yield a near-optimal solution using greedy feature selection. Second, our submodular quality function criterion can be integrated into gSpan, the state-of-the-art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs even during frequent subgraph mining.