1. Higher-Order Explanations of Graph Neural Networks via Relevant Walks.
- Author
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Schnake, Thomas, Eberle, Oliver, Lederer, Jonas, Nakajima, Shinichi, Schutt, Kristof T., Muller, Klaus-Robert, and Montavon, Gregoire
- Subjects
QUANTUM chemistry ,SENTIMENT analysis ,EXPLANATION ,DATA analysis ,FEATURE extraction - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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