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arXiv · 2006.03589

Higher-Order Explanations of Graph Neural Networks via Relevant Walks

Thomas Schnake·Oliver Eberle·Jonas Lederer·Shinichi Nakajima·Kristof T. Schütt·Klaus-Robert Müller·Grégoire Montavon
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Abstract

Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI approaches are not applicable. To a large extent, GNNs have remained black-boxes for the user so far. In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions, i.e. by identifying groups of edges that jointly contribute to the prediction. Practically, we find that such explanations can be extracted using a nested attribution scheme, where existing techniques such as layer-wise relevance propagation (LRP) can be applied at each step. The output is a collection of walks into the input graph that are relevant for the prediction. Our novel explanation method, which we denote by GNN-LRP, is applicable to a broad range of graph neural networks and lets us extract practically relevant insights on sentiment analysis of text data, structure-property relationships in quantum chemistry, and image classification.

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Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon. 2020-06-05. Higher-Order Explanations of Graph Neural Networks via Relevant Walks. https://doi.org/10.1109/tpami.2021.3115452

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