Learning from Shortcut: A Shortcut-guided Approach for Graph Rationalization
Keywords: graph rationalization, shortcut learning
Abstract: The remarkable success in graph neural networks (GNNs) promotes the Graph Rationalization methods that aim to provide explanations to support the prediction results by identifying a small subset of the original graph (i.e., rationale). Although existing methods have achieved promising results, recent studies have proved that these methods still suffer from exploiting shortcuts in the data to yield task results and compose rationales. Different from previous methods plagued by shortcuts, in this paper, we propose a Shortcut-guided Graph Rationalization (SGR) method, which identifies rationales by learning from shortcuts. Specifically, SGR consists of two training stages. In the first stage, we train a shortcut guider with an early stop strategy to obtain shortcut information. During the second stage, SGR separates the graph into the rationale and non-rationale subgraphs and lets them learn from the shortcut information generated by the frozen shortcut guider to identify which information belongs to shortcuts and which does not. Finally, we employ the non-rationale subgraphs as environments and identify the invariant rationales which filter out the shortcuts under environment shifts. Extensive experimental results on both synthetic and real-world datasets clearly validate the effectiveness of our proposed method.
Primary Area: representation learning for computer vision, audio, language, and other modalities
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Submission Number: 6956
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