EiX-GNN：图神经网络的概念级特征中心性解释器
EiX-GNN : Concept-level eigencentrality explainer for graph neural networks
摘要：
目前，深度预测模型，特别是图神经网络，在关键应用中占有重要地位。在这种情况下，这些模型需要高度可解释或可解释的人类，在社会范围内，这种理解对于那些在需要解释的模型和背景中没有很强先验知识的人来说也是可行的。在文献中，解释是人类对现象的知识在解释者和被解释者之间的传递过程。我们提出了EiX-GNN（Eigencentrality eXplaner for Graph神经网络）一种新的强大的方法来解释图神经网络，该方法通过计算编码这种解释过程中潜在的社会解释者对解释者的依赖性。为了处理这种依赖关系，我们引入了被解释者概念相似性的概念，它允许解释者根据被解释者的背景或期望来调整解释平面。我们领导一个定性研究，以说明我们的解释概念的相似性概念在现实世界的数据以及定性研究，比较，根据客观指标建立在文献中，公平性和紧凑性，我们的方法相对于performingstate-of-the-art方法。事实证明，我们的方法在这两个方面都取得了很好的效果。
英文摘要：
Nowadays, deep prediction models, especially graph neural networks, have a majorplace in critical applications. In such context, those models need to be highlyinterpretable or being explainable by humans, and at the societal scope, this understandingmay also be feasible for humans that do not have a strong prior knowledgein models and contexts that need to be explained. In the literature, explainingis a human knowledge transfer process regarding a phenomenon between an explainerand an explainee. We propose EiX-GNN (Eigencentrality eXplainer forGraph Neural Networks) a new powerful method for explaining graph neural networksthat encodes computationally this social explainer-to-explainee dependenceunderlying in the explanation process. To handle this dependency, we introducethe notion of explainee concept assimibility which allows explainer to adapt itsexplanation to explainee background or expectation. We lead a qualitative studyto illustrate our explainee concept assimibility notion on real-world data as wellas a qualitative study that compares, according to objective metrics established inthe literature, fairness and compactness of our method with respect to performingstate-of-the-art methods. It turns out that our method achieves strong results inboth aspects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
