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
Ensemble learning is a powerful approach to improving model performance in addressing big data challenges.It first trains multiple base models with existing machine learning algorithms and then combines their outputs ...Ensemble learning is a powerful approach to improving model performance in addressing big data challenges.It first trains multiple base models with existing machine learning algorithms and then combines their outputs to yield the final prediction result.Despite the demonstrated potential of current ensemble strategies,their integration primarily relies on unidimensional metrics for each base model(e.g.,prediction accuracy),which are too coarse-grained to adequately represent the multifaceted abilities of models.In this paper,we propose MADE,a novel Multidimensional Ability aware Dynamic Ensemble paradigm by drawing upon the fine-grained and well-developed measurement of human learning.Specifically,MADE incorporates a dynamic ensemble algorithm that modifies the models’weights in accordance with their evolving abilities throughout the training process.To evaluate the multidimensional abilities of the base models,we develop a diagnostic module that captures individual base models’latent knowledge levels.Besides,an ensemble weight inductor is designed in MADE to generate individual ensemble weights for each sample,different from previous ensemble methods that assign the same ensemble weights to all samples.Extensive experiments on diverse datasets demonstrate the effectiveness of our proposed model in achieving improved ensemble performance.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.
基金supported by the National Natural Science Foundation of China(No.62337001)Anhui Provincial Natural Science Foundation(Nos.2308085MG226 and 2308085QF229)the Fundamental Research Funds for the Central Universities.
文摘Ensemble learning is a powerful approach to improving model performance in addressing big data challenges.It first trains multiple base models with existing machine learning algorithms and then combines their outputs to yield the final prediction result.Despite the demonstrated potential of current ensemble strategies,their integration primarily relies on unidimensional metrics for each base model(e.g.,prediction accuracy),which are too coarse-grained to adequately represent the multifaceted abilities of models.In this paper,we propose MADE,a novel Multidimensional Ability aware Dynamic Ensemble paradigm by drawing upon the fine-grained and well-developed measurement of human learning.Specifically,MADE incorporates a dynamic ensemble algorithm that modifies the models’weights in accordance with their evolving abilities throughout the training process.To evaluate the multidimensional abilities of the base models,we develop a diagnostic module that captures individual base models’latent knowledge levels.Besides,an ensemble weight inductor is designed in MADE to generate individual ensemble weights for each sample,different from previous ensemble methods that assign the same ensemble weights to all samples.Extensive experiments on diverse datasets demonstrate the effectiveness of our proposed model in achieving improved ensemble performance.
