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Measuring model performance is a key issue for deep learning practitioners.However, we often lack the ability to explain why a specific architectureattains superior predictive accuracy for a given data set. Often, validationaccuracy is used as a performance heuristic quantifying how well a networkgeneralizes to unseen data, but it does not capture anything about theinformation flow in the model. Mutual information can be used as a measure ofthe quality of internal representations in deep learning models, and theinformation plane may provide insights into whether the model exploits theavailable information in the data. The information plane has previously beenexplored for fully connected neural networks and convolutional architectures.We present an architecture-agnostic method for tracking a network's internalrepresentations during training, which are then used to create the mutualinformation plane. The method is exemplified for graph-based neural networksfitted on citation data. We compare how the inductive bias introduced ingraph-based architectures changes the mutual information plane relative to afully connected neural network.

Mutual information estimation for graph convolutional neural networks