探索图可解释性中的分布外泛化问题.pdf

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1、探索图可解释性中的分布外泛化问题方俊峰 中国科学技术大学 博士DataFunSummit#2023当前的可解释评估指标真的“公平”吗？可解释算法为何会引入OOD问题？如何实现网络-数据的联合解释？1)避开公式 2)中英混杂2Background3How to define explainability?(1)Miller,Tim.“Explanation in artificial intelligence:Insights from the social sciences.”arXiv Preprint arXiv:1706.07269.(2017).Explainability is th

2、e degree to which a human can understand the models result 1Find which fractions are most influential to the GNNs predictionFind important subgraphInputModelCycleCycleGridHouseBA3-motifExplanationMethodExisting methodsSAGradCAMGNNExplainerPGExplainerCF-GNNExGraphMaskGSATDIR*GREA*m=0.03m=0.94GemPGMEx

3、6Evaluation metricNIPS 2023 Oral(2%)Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis.Existing Evaluation metrics1.Human supervision seeks to justify whether the explanations align with human knowledge,but it is often too subjective,thus hardly providing quantifiable

4、 assessments.2.Measuring the agreement between the generated and ground-truth explanations,such as Precision and Recall.Unfortunately,access to the ground truth is usually unavailable and labor-extensive.CycleGridExisting Evaluation metrics#Caveat of RM:as the after-removal subgraphs are likely to l

5、ie off the distribution of full graphs,the GNN is forced to handle these off-manifoldinputs and easily gets erroneous predictions.3.Feature Removal(RM):first remove the unimportant features and feed the remaining part(i.e.,explanatory subgraph)into the GNN,and then observe how the prediction changes

6、.Existing Evaluation metrics4.Generation-based metrics:Instead of directly feeding,they use a generative model to generate a new full graph conditioned on the subgraph.#Caveat of Generation-based metrics:the generation-based metrics show respectto the data distribution somehow but couldbe inconsiste