The remarkable success in graph neural networks(GNNs)promotes the explainable graph learning methods.Among them,the graph rationalization methods draw significant attentions,which aim to provide explanations to suppor...The remarkable success in graph neural networks(GNNs)promotes the explainable graph learning methods.Among them,the graph rationalization methods draw significant attentions,which 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.Then SGR 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 experiments conducted on synthetic and real-world datasets provide clear validation of the effectiveness of the proposed SGR method,underscoring its ability to provide faithful explanations.展开更多
基金supported by grants from the Joint Research Project of the Science and Technology Innovation Community in Yangtze River Delta(No.2023CSJZN0200)the National Natural Science Foundation of China(Grant No.62337001)and the Fundamental Research Funds for the Central Universities.
文摘The remarkable success in graph neural networks(GNNs)promotes the explainable graph learning methods.Among them,the graph rationalization methods draw significant attentions,which 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.Then SGR 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 experiments conducted on synthetic and real-world datasets provide clear validation of the effectiveness of the proposed SGR method,underscoring its ability to provide faithful explanations.
