Higher-Order Explanations of Graph Neural Networks via Relevant Walks

• URL: http://arxiv.org/abs/2006.03589v3
• Date: Fri, 27 Nov 2020 04:10:00 GMT
• Title: Higher-Order Explanations of Graph Neural Networks via Relevant Walks
• Authors: Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Sch\"utt, Klaus-Robert M\"uller, Gr\'egoire Montavon
• Abstract summary: Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions. We extract practically relevant insights on sentiment analysis of text data, structure-property relationships in quantum chemistry, and image classification.
• Score: 3.1510406584101776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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