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Semi-supervised feature selection using co-occurrent frequent subgraphs

In the real world, there exist many graph applications that have only a small number of labeled graph data. Graph classification can achieve desired performances only if enough amounts of labeled data are available. Semi-supervised feature selection studies have been proposed to solve the short labeled data problem. However, the existing studies still do not meet satisfactory classification accuracy. In this paper, we propose a novel semi-supervised feature selection method, named co-occurrent graph feature selection (SCGFS) that selects a set of optimal co-occurrent frequent subgraph features for graph classification. Co-occurrent subgraphs can have higher discriminative powers than those of individual frequent subgraphs. Since co-occurrent patterns have an inefficiency problem, we propose a branch-and-bound search that reduces the feature search space with effective pruning techniques. Through comprehensive experiments, we show that the proposed framework can efficiently select more discriminative subgraph features compared with existing semi-supervised feature selection algorithm.