Multiple Testing Correction in Graph Mining

We present a method for finding all subgraphs whose occurrence is significantly enriched in a particular class of graphs while correcting for multiple testing. Although detecting such significant subgraphs is a crucial step for further analysis across application domains, multiple testing of subgraphs has not been investigated before as it is not only computationally expensive, but also leads to a great loss in statistical power. Here we solve both problems by examining only testable subgraphs, which dramatically reduces the number of subgraph candidates, yet all significant subgraphs are detected. Moreover, we exploit the dependence between testable subgraphs by considering the effective number of tests to further increase the statistical power. Our experiments show that the proposed methods are faster and are statistically more powerful than the current state-of-the-art approach.

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