Significant subgraph mining for neural network inference with multiple comparisons correction

Abstract

We describe how the recently introduced method of significant subgraph mining can be employed as a useful tool in neural network comparison. It is applicable whenever the goal is to compare two sets of unweighted graphs and to determine differences in the processes that generate them. We provide an extension of the method to dependent graph generating processes as they occur, for example, in within-subject experimental designs. Furthermore, we present an extensive investigation of the error-statistical properties of the method in simulation using Erdős-Rényi models and in empirical data in order to derive practical recommendations for the application of subgraph mining in neuroscience. In particular, we perform an empirical power analysis for transfer entropy networks inferred from resting-state MEG data comparing autism spectrum patients with neurotypical controls. Finally, we provide a Python implementation as part of the openly available IDTxl toolbox.